Spectral Compressive Imaging via Chromaticity-Intensity Decomposition
Authors: Xiaodong Wang
Zijun He
Ping Wang
Lishun Wang
Yanan Hu
Xin Yuan
Venue: NeurIPS 2025 poster ()
Keywords: computational imaging
computational photography
Primary area: applications

Abstract: In coded aperture snapshot spectral imaging (CASSI), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreover, the captured radiance inherently depends on scene illumination, making it difficult to recover the intrinsic spectral reflectance that remains invariant to lighting conditions. To address these challenges, we propose a chromaticity-intensity decomposition framework, which disentangles an HSI into a spatially smooth intensity map and a spectrally variant chromaticity cube. The chromaticity encodes lighting-invariant reflectance, enriched with high-frequency spatial details and local spectral sparsity. Building on this decomposition, we develop CIDNet—a Chromaticity-Intensity Decomposition unfolding network within a dual-camera CASSI system. CIDNet integrates a hybrid spatial-spectral Transformer tailored to reconstruct fine-grained and sparse spectral chromaticity and a degradation-aware, spatially-adaptive noise estimation module that captures anisotropic noise across iterative stages. Extensive experiments on both synthetic and real-world CASSI datasets demonstrate that our method achieves superior performance in both spectral and chromaticity fidelity. Code is released at: \url{https://github.com/xiaodongwo/CIDNet}.

Spectral Compressive Imaging via Chromaticiy-IItensity Decomposition
Xiaodong Wang 1.2 Zijun He 1,2 Ping Wang Lishun Wang Yanan Hu 1,2 Xin Yuan" 2,* 1 Zhejiang University, School of Engineering. Westlake University,
3 Chengdu Institute of Biology, Chinese Academy of Sciences
Abstract
In coded aperture snapshot spectral imaging (CASST), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreover, the captured radiance inherently depends on scene illumination, making it difficult to recover the intrinsic spectral reflectance that remains invariant lighting conditions. To address these challenges, we propose a chromaticity work, which disentangles an HSI into a spatially smooth intensity map and a spectrally variant chromaticity cube. The chromaticity encodes lighting hightinginnvariant invariant reflectance, enriched with feeuffreyeency spatial details and local spectral sparsity. Building on this decomposition, we develop CIDNet-a ChrromaticityInneent Decomposition unfolding network within dual-camera CASSI system. CIDNet integrates a hybrid spatial-spectral Transformer tailored to reconstruct fine-grained and sparse spectral chromaticity and a degradation-avee aware, apatially-adaptivy noise estimation module that captures anisotropic noise across iterative stages. Extensive experiments on both synthetic and real-world CASSI datasets demonstrate that our method achieves superior performance in both spectral and chromaticity fidelity. crrpiuub.comm/xiadoogggooCCCCCC
decomposition frame-
Code is released at:
Introduction
Coded aperture snapshot spectral imaging (CASST) has emerged as promising architecture for capturing hyper-
spectral images (HSIs) in a single shot [I 25 34]. By
jointly modulating the spectral cube with coded aperture and dispersing spatially through a prism, CASSI produces 2D compressed measurement that encodes both spatial and spectral information. This compressive measurement fuses (shears) the spectral bands, making each pixel of the 2D sensor a mixture of many wavelengths. As a result, recovering the full 3D spectral image becomes severely under-determined, ill-posed inverse problem.
Spectral Image
Chromaticty
Intensity
[b)
W
Highlihht
Lowlight
Texture
Figure 1: (a) Chrmaticity-Intensity de- composition of HSI images (b) Chromatic- ity exhibits highlight removal, lowlight en- hancement and fiequfreyeency textures.
The difficulty comes mainly from two aspects. One is that spatial and spectral signals are highly overlapped and entangled the compressed measurement. Many works attempt to address this through various priors deep models, broadly categorized into four paradigms. Optimization- based methods [17 33 introduce hand-crafted priors such as total variation or low-rank constraints.
"Corresponiing Author: Xin Yuan (xyuan westlake.edu.cn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

However, their performance is often limited in recovering spatial structures, especially under complex textures or noise. Plug-and-Play (PnP) approaches [24 39 integrate powerful pre- pre-trained denoisers into iterative solvers, yet these methods typically denoise each or few spectral band independently, neglecting spectral correlation and structure. Deep unfolding methods 4 7
14
19 37 38 bridge
model-based and data-driven paradigms by learning iterative modules guided by the CASSI physics.
End-to-end networks 3 10 20 21 26 leverage CNN or Transformer to directly infer the spectral
cube from measurements. These deep learning-basse frameworks implicitly exploit spatial-spectral dependencies and have shown promising performance. Recently, diffusion model [22 30 [5] and Mamba 23] have been used for spectral reconstruction. Nonetheless, all these approaches rely on network backbones to learn interpretable decomposition or quantitative structure that explicitly characterizes the physical roles of spatial and spectral components during reconstruction.
features in an implicit manner. There lacks a clear and
The second challenge is that existing methods often overlook the impact of illumination. Since the captured spectral measurement radiance-based, it inherently entangles the intrinsic surface reflectance with scene illumination. This coupling makes the reconstruction sensitive to lighting variations across time and environments, thereby limiting spectral accuracy. To address similar issues in the RGB image, prior works have explored intrinsic image decomposition 12 g 15 16
and Retinex- based models [28] 29 to explicitly separate reflectance from illumination, enabling applications such as shadow removal and low-light enhancement. In the hyperspectral remote sensing community, several studies have also extended intrinsic decomposition to spectral reflectance and illumination separation
32 offering better invariance to lighting conditions. However, the best of our knowledge, such decomposition has not yet been incorporated into CASSI reconstruction.
In this paper, we propose a novel chromaticity learning framework for compressive spectral imaging, which leverages a bbrommtiiity-intensitt decomposition prior under the CASSI sensing mechanism. Our motivation is illustrated in Fig. where the spectral image cube X is factorized as:
X = C O I,
where C denotes the chromaticity cube and represents the intensity image. Notably, C exhibits several desirable properties: (i) spatially invariant to illumination, suppressing highlights and enhanc- ing details in low-light regions; (ii) spectrally sparse with localized support (as illustrated latter); (iii) enriched with figh-frreuency texture, essential for fine-detail recovery. In contrast, the intensity component I captures the global illumination structure in the scene. It is interesting to note that the chromaticity exhibits more intrinsic characteristics of the sample compared to hyperspectral images. Hence, learning the chromaticity instead of HSIs seems to benefit the field more.
Building upon the above observations, we propose physically interpretable decomposition model tailored for CASSI systems. By leveraging a dual-camera CASSI setup, we validate this decomposition paradigm within both traditional optimization-basse unfolding frameworks. To further explore its potential, we design a novel Chromaticity-Intenisi Decomposition Network (CIDNet), which incorporates the spectral sparsity of chromaticity through sparse TopK spectral Transformer, and models spatially anisotropic noise via a degradation- spatially-adaptive variance estimator.
solvers and deep
In summary, our main contributions are summarized as follows:
i) We propose a novel chromaticity-intentt decomposition model for spectral compressive imaging, which explicitly separates hyperspectral images into lighting-invariant chromaticity and smooth intensity. We further validate its effectiveness on optimization-based and unfolding algorithms in a dual-camera CASSI setting.
ii) We develop an ditensity-guider deep unfolding network that incorporates the chromaticity decomposition into unfolding algorithm. The network features hybrid spatial-speetral Trans- former (HSST) architecture, where the encoder leverages based local spatial attention (Spa-LWSA) and the decoder employs sparse TopK spectral attention (Spec-TKSA) to capture localized spectral structures.
iii We introduce a degradation-
spatially-adaptive dual noise estimation module (DNEM) to model anisotropic noise across different reconstruction stages. This module enables each iteration to adaptively handle varying noise levels across spatial locations.
iv) Extensive experiments on both synthetic and real datasets demonstrate that our method achieves state-of-the-an performance terms of spectral reconstruction and chromaticity fidelity.

---

Elomart mu muttipication
w/A3
Watrix multppication
p
proje)
Weighted
(r.R)
5:
Down-
(0)
Up-
sample
Conv3x3
Digensoo
LN Layorhiom
LWSA
Spec-
Convolution
Down
Cuber
Spa-LWSSA
Rucion
Spec-TKSA
Softema
Down
Up
Curt
Spa LWSA
Figure 2: (a) The architecture of our CIDNet with stages (iterations). (b) The CASSI system uses an intensity-guided mask to modulate the chromaticity. (c) Diagram of asymmetric backbone for our hybrid spatial-spectral Transformer (HSST), with local window spatial attention (Spa-LWSA) in Encoder and sparse TopK spectral atlention module (Spec-TKSA) in Decoder. (d) Details of Spec-TKSA.
Proposed Method
2.1 Degradation Model of CASSI
Inspired by chromaticity-intenttt
decomposition in RGB intrinsic image analysis, we extend this
concept to the hyperspectral domain. Given a hyperspectral image cube X E @ W xNN, decompose it into a spatially smooth intensity image I E ## and chromaticity cube B as:
X(u, u, A) = C(u, v, A) @ I(u,
(2)
where (u, v) denotes spatial location, is the spectral band index, and @ denotes pixel-wise multipli- cation. Specifically, the intensity image is defined as the average spectral energy per pixel:
I(u, v) N Σ X(u, e, A),
(3)
We found that intensity image can be approximated as a PAN image in dual-camera CASSI. A detailed proof in this PAN-Intensity Equivalence is provided in supplement materials. Hence, the chromaticity is computed as the normalized spectral signature:
X(u,o.A) C(u, v, A) I(a,r)++>
(4)
where is small constant to avoid division by zero. This decomposition separates the multiplicative effect of illumination I(u, v) from the spectral reflectance C(u, v, A) which captures intrinsic scene properties. Importantly, C is invariant changes in illumination intensity and direction, enabling more robust modeling reflectance and spectral reconstruction under varying lighting conditions (see supplement materials). After decomposition, the CASSI measurement process can be modeled as follows. The hyperspectral cube is modulated by a coded aperture M # rrsuutting in spatially coded cube:
X'(u, v. A) = C(u, I(u,v) M(u,v.
(5)
Now we can treat I(u, v) M v)) as new formation of coded mask M' v) = I(u, v) M v) incorporating the spatial intensity, we call it sitensity-guided mask. This leaves the chromaticity an unknown variable when the intensity is obtained beforehand. Following typical CASSI formulation, the modulated cube X' then passed through a dispersive element that shifts each spectral band Ana by wavelength-dee -ewelength-dependeet displacement d(>ns Xe) along the spatial axis (e.g., g., the z-axis). The sheared datacube can be expressed as:
x" (u, X'(u, d(Ann Anx):
(6)
3

---

RGB
Measurement
G-0
Spectral density curves
DeSCI
GAP-Net
MST
DAUHST
SSR
PIDS
In2SET
CIDNet-9stg
Figure 3: Simulation HSIs reconstruction comparisons of Scene 7 with 4 (out of 28) spectral channels. The
left shows the spectral curves corresponding to the two red boxes of the RGB image. The top-right depicts the enlarged patches corresponding to the yellow boxes in the bottom HSIs. Zoom in for a better view.
where de the reference wavelength that remains unshifted. Finally, the 2D measurement E I (W +d-N+x acquired by the camera a summation of all dispersed bands:
Yu,, v) Σ
NA mx= (u, v, nx) + N(u, v).
where is additive measurement noise. This can be written compactly in vectorized form as:
y (c + i) +
(8)
where c = vec vec(C). (C), i= vec(T), 4 is the sensing matrix determined by the modulation and dispersion process, and is the vectorized noise term. Given that the intensity map I is known (PAN RGB image in dual-camera CASSI scheme), we formulate the chromaticity- based measurement model as standard linear inverse problem:
y= Hc + n,
(9)
where denotes the vectorized chromaticity, H is the effective sensing matrix that incorporates both the CASSI modulation-diisperiio process and the known intensity modulation, and the vectorized noise.
2.2 Optimization Framework of CASSI
To characterize realistic imaging noise, we sume an anisotropic Gaussian noise model n ~ (00, Σ), where Σ diag(of....,, agonal covariance matrix whose diagonal en- tries of represent the noise variance the i-th pixel. This implies that the noise is spatially varying but uncorrelated across pixels. Under Bayesian framework, the posterior probability of the chromaticity e is given by p(c y) x ply c) p(c). and the likelihood is:
Table 1: Data-consistency projection comparison.
Method
Gradient projection updating
is di-
ISTA GAP HQS
ch+l z H (y HHz))
c+tl z (HH
>- Hz
chel z (HH I (l)) "(y
Ours
ch+l
(HI (EE) "y Hz
ply c, Σ) exp 4 Hc) x (y-(yyy-H))) -Hc),,
(10)
and p(c) exp(- exp(-rR(c)) is generic prior chromaticity with regularization function R(-)
and weight > 0. Maximizing the posterior leads to the following Maximum A Posteriori (MAP)
estimation problem:
è = argming [y - Hc) - 'y Hc) + +R(c).
(11)
When the noise is homoscedastic (i.e., 0; = 1), the problem reduces to the common quadratic form: è argming |ly Hcll + 7 R(().. We rewrite Eq. (11) using an auxiliary variable z:
è, é argmin ( He) - -y
Hc) + 7 R(z),
c=
(12)
Using the half quafrauadr splitting (HQS) framework. Eq. is minimized by solving the following Cata-consistency and data-prior subproblems itcratively:
(k+1) argminn {y ly- Hc) T 1
Hc) f 20x)
(13)
z (k+1) = argming ||z c(++1) 1| R(2).
(14)

---

where is penalty parameter and k represent kth iteration. The c-subproblem in Eq. quadratic and has closed-form solution:
13 is
c = H H MT)
Eyy + pzz (m)
(15)
where represent identity matrix. Note that H HI is a fat matrix and HHE v() will be difficult compute and thus we simplify it based on the Ceerman-MorrioonWWee Woodbury formula,
H Σ H -- E + "HH H.
(16)
In CASSI systems, HH is diagonal matrix defined HH - diagfhi..... h With Σ - diag(of... ...oof.................. R and detailed derivation supplement materials, we obtain a generalized form of gradient projection, which is expressed as,
( = zz(1) + H (HH + +PE) Hz
(17)
Define this gradient projection as c(k+1) (.). Interestingly, we observed that this gradient projection resembles previous optimization- based methods but introduces a key change relates to the ppatially-varyinn noise modeling. As summarized in Tab. [ while traditional ISTA (36), GAP [19], and HQS methods employ static cata-consist steps with fixed regularization, our method proposes dynamic, spatially-adaptiie correction mechanism. Finally, we update (k+11) using any proximal operator depending on the prior R((-).
(++1) prox, /u. (C (R)
(18)
If the noise variance E is known a priori, with Eq. (17 and Eq. (8), this concludes the efficient HQS derivation with anisotropic Gaussian noise. However, in practice, the noise map is unavailable and may vary dynamically across iterations (e.g., in PnP or unfolding methods). To effectively account for the Egrradation-varinnn characteristics in the CASSI system, we parameterize the anisotropic noise covariance (k) and the denoising strength Tk/ pa in stage-specific manner. Both parameters learned by degradation-aware estimator & that takes input the current iterate (R) and the measurement y:
(k)) 7e/p4) = {(2()),)
(19)
The estimator & is implemented as a lightweight CNN that jointly captures spatial structure in (*) and the encoded degradation in y. A detailed network module is found in supplement materials. We denote (k) and (R) 7(k) (k) as the noise map for gradient projection and proximal mapping (denoiser) respectively (Dual Noise-Estimatie
Module (DNEM), as we refered to). The estimated (A) reflects the anisotropic uncertainty in the current iterate, and modulates the linear update of c(k-1) via Eq. (17. while (k) controls the noise level fed into the proximal denoiser for (k+1) via The final iterative process can be expressed as:
Eq.
18
[ (4) (k)) E(z(k),)) ,y),
(k+1) projzcs (n) (z (8)) = (kk) + H (HH + 110)) --4 -Hz(k),
(20)
(k+11 -rox www.nnnche (2 (k+)))
Here, (k) is omitted due to the usage of the network, R(-) is regularization prior, which could be total variation, or a learned denoiser as in our experiments. This stago-adaptive formulation enables flexible and efficient recovery under spatially variant degradation patterns.
To validate the effectiveness of our chromaticity-intensiyy decomposition strategy, we explore two integration paradigms: a traditional model-based iterative scheme and a deep unfolding network. The classical iterative algorithm is described in supplement materials, leveraging analytical priors and explicit update rules based on the degradation model. In contrast, our primary design adopts a learnable unfolding structure, as illustrated in Fig. 2 Each stage composed of a learnable noise estimation, analytical reconstruction step shown in Eq. (17) and a learned proximal denoiser. This framework offers the interpretability of traditional optimization while benefiting from the expressiveness and efficiency of deep networks.
5

---

Rod
Neveloghh
Mestinghh
Deroety
530
504
c40.0
DeSCI
PIDS
GAP-Net
MST
DAUHST
SSR
In2SET CIDNet-9stg
GT
Figure 4: Simulation: chromaticity reconstruction of Scene with 4 (out of 28) spectral channels. The spectral curves correspond to the two red boxes in the RGB image (top-middle). The top-right depicts the zoomed patches corresponding the yellow boxes the bottom chromaticity.
2.3 Hybrid Spatial-Spectral Transformer
To better reconstruct the chromaticity component which inherently contains rich spatial textures and locally correlated spectral patterns (illustrated in Fig. (5) we propose an asymmetric UNet backbone using Hybrid Spatial-Spectral Transformer (HSST). This module is specifically designed to
simultaneously learn the high-frequency details in spatial dimensions and sparse-local dependencies in the spectral domain, as shown Fig. 2
We adopt dual-branch design: the spatial attention branch captures intra-image textures through Swin Transformer Encoder, while the spectral attention branch is tailored exploit sparse and locally correlated spectral features using a TopK spectral attention mechanism in Decoder. This asymmetric design inspired by [37] and motivated by experimental verification, where the asymmetric design has better reconstruction results.
Spectral Attention. Unlike the spectral correlation in HSIs, chro- maticity spectra features exhibit structured sparsity and localized correlation, see Fig. 5 Motivated by this, we introduce window- based spectral TopK attention mechanism, where attention is applied across the spectral channels within each local spatial window. Specif- ically, each spectral token attends only to its K most relevant spectral neighbors, enforcing both sparsity and locality.
Figure 5:
Demonstration
sparse and local spectral correla- tion of chromaticity. Top: RGB contents of the benchmark testing data. Middle: spectral correlation coefficient matrices of the HSIs (28x28), Bottom: Corresponding matrices by the chromaticity.
Given an input feature cube X E HX we first divide it into non-overlapping spatial windows of size N x N, resulting in batch of local cubes (X C R NºC. Within each window, we perform spectral self-attention across the C' channels for every spatial location. We begin by computing the query, key, and value embeddings using learned
convolutions:
{ (Q,, K,, Vi} Convixi (X,) N
(21)
where is the embedding dimension per head. To model inter-channel dependencies, we transpose the last two dimensions and perform attention along the channel axis. For each position i E (1,..., N2). the attention computed as:
A, Softmax
(TopX( Q.K. Vd
E BCC.
(22)

---

Table 2: Comparisons of HSIs between CIDNet and SOTA methods on KAIST simulation dataset. PSNR (upper entry in each cell), and SSIM (lower entry in each cell) are reported. The best result is highlighted in bold.
Method
Params(MM
GFIOPs(G)
S1
S2
S3
84
85
S6
S7
S8
S10
Avg
27.13
23.04
26.62
34.96
23.94
22.38
24.45
22.03
24.56
23.59
25.27
DeSCI 17
0,748
0.620
0,818
0.897
0.706
0.0.683
0.743
0.673
0.732
0.587
0.721
33.74
33.26
34.28
41.03
31.44
32.40
32.27
30.46
33.51
30.24
33.26
GAP-Net 19
4.27
78.58
0.911
0,900
0.929
0.967
0.919
0.925
0.902
0.905
0.915
0.895
0.917
MST-L X
2.03
28.15
35,40
35.87
36.51
42.27
32.77
34,80
33.66
32.67
35.39
32.50
35.18
0.941
0,944
0.953
0.973
0.947
0.955
0.925
0,948
0.949
0.941
0.948
37,25
39.02
41.06
46 15
35,80
37.08
37.57
35 10
40.02
34.59
38.36
DAUHST-9ktg
6.15
79.50
0.958
0.967
0.971
0.983
0.969
0.970
0.963
0,966
0.970
0.956
0.967
39.07
42.04
44.49
48
38.64
38.50
39. 16
36.96
43.12
36.08
40.69
SSR 9sg
38
5.18
78.93
0.970
0,981
0,980
0.990
0.980
0.978
0.971
0.976
0,980
0.968
0.978
PIDS-RGB 3
42.09
40.08
41.50
48.55
40.05
39,00
36.63
37.02
38.82
38.64
40.24
0.983
0.949
0.968
0.989
0.982
0.974
0.940
0.948
0,953
0,980
0.967
In2SET-9stg x7
9.69
59.40
42.56
46.42
44.55
50.63
42,01
42.49
41.59
40.53
43.83
42.33
43.69
0.994
0.986
0.992
0.991
0.983
0.989
0.994
0.990
40.88
39
43.55
47.54
40.37
41.94
40.98
41.11
42.52
40.79
42.51
CIDNel-3dg
1.40
24.80
0.986
0.993
0,983
0.993
0.990
0
0.981
0.992
0,987
0.992
0.989
CIDNet-5stg
2.33
41.26
41.56
46.36
43.98
47.92
41.47
42.27
41.27
41.36
43.90
40,56
43.07
0.987
0.994
0.984
0.993
0.992
0.992
0,982
0.992
0.990
0.992
0.990
CIDNet-7istg
3,26
57.71
46.77 79
44.52
48.51
41.44
42.56
41.46
41.93
44.36
41.49
43.47
0.988
0.995
0.985
0.994
0.992
0.993
0.983
0.993
0.990
0.993
0.990
42.72
47.88
44.87
48.83
42.59
43.01
42.28
42.26
44,68
42.05
44.12
CIDNet-9dgg 4.19
0.990
0.996
0.986
0.994
0.993
0.993
0.985
0.994
0.991
0.994
0.991
where Top retains only the Topk values per row and masks out the rest with -oo before applying softmax. This yields a sparse attention map across spectral channels for each spatial location i in the window Z; A,V; E RCxd To improve the robustness and expressiveness of spectral modeling, we further adopt a multi-ratio strategy. Instead of selecting a single sparsity level, we generate multiple attention maps using different TopK ratios (e.g.. {1/2, 2/3, 3/ 4, 4/5} of C), and then aggregate them adaptively. Specifically, let (rr) denote the sparse attention computed under ratio r, and (+) be learnable scalar weight. The final attention output is then formulated as:
Z, = EPER (r)) Softmax(A VE.
(23)
where R. denotes the set of TopK ratios. This fusion across multiple sparsity levels enables the network to capture both dominant and complementary spectral correlations, further improving spectral detail preservation. and reshaped back to the original window structure. After processing all windows, the outputs are stitched to reconstruct the full feature map. This spectral Topk attention not only reduces the computational burden from dense O(C²) to O(KC), but also explicitly captures the structured sparsity observed in reflectance spectra- where only a few wavelengths contribute significantly. Empirically, we find that this strategy enhances spectral sharpness and suppresses irrelevant cross- band mixing. Following the standard Transformer architecture, we apply conventional feedforward network after the sparse Topk attention, which is not the focus of our work.
Spatial Attention. Inspired by recent advances in Swin Transformers 18 we employ the Swin Transformer as our spatial modeling backbone. The imput feature map X E F x XXx divided into non-overlapping windows of size M x M. Within each window, we perform multi-head self-attention (MSA) by computing
Z Zupa = MSA (LN(X) + X, Zum = FFN (Lapa)) + Zepa:
(24)
where L-(-) denotes layer normalization, and FFN is a standard feedforward network. The relative position bias and winddow mechanism in Swin Transformer enhance local texture modeling while maintaining global continuity across windows.
3 Experiments
We conduct comprehensive experiments on both simulated and real-world CASSI systems. The datasets, training settings, and implementation details are introduced as follows.
7

---

Table 3: Comparisons of intensity (left) and chromaticity (right) between CIDNet and SOTA methods on KAIST simulation dataset. PSNR (upper entry in each cell), and SSIM (lower entry in each cell) are reported.
Mathed
52
53
94
55
56
57
59
S10
Aag
D6N
17
79.4314476
0.5481.99
02
0,720,67
91.01/3555
0.20003
41.7119.51 51
0,970.75
26.2220014 14
0.8LA172
151221665
0,790.50
27,08/0:87
0.528.60
34.5519.55
0.770554
28,
0,869079
19.91480
0.690.32
28, 787781991 79
0,220.62
GAP-Nc 15
36,78/2855
02550062
35,54/2055
0.339.61
39.31/25.6
0.960.79
45.9W1863
0.989,66
33,94/23.0 20
0.948176
3437/1943
0950057
35,65/2433
0.95/..4
32,9/29.11
0.440.44
57.08/7.34
0,900.52
32,05/3.199
0.220.39
36,11/20.50
0.950.61
L
36,05223005 05
01660.72
584229022
41,0782.56
47.5319.5
0.990.75
35,1422.00 50
0.96A184
66,422966
57,0422.188 18
0.96AU75
34 34892599
09600.5
94
902663165
34 36.27
0550760
58.5515561
03900766
39.68/2.722
50
45,0834588
492441554
37,8413.08
3877N3263
20,338395
367781.29
44,5503430
3591/2888
05980.85
09600.9
05900.00
09963.66
15R-9512 35
411193445
0093083
43.3831
0.900.98
0.990.97
60
0.900.9
0.96
46
0980.92
0.98/086
82
0960091
0.710.36
0.980.01
PIDS
37,44/18.20
0990173
38.91117227
0.989.35
3488/22774
90
0.901661
30
51
37.0616.34
OUSBRUS
35.43/3.7
0990015
102SET 94g 21
5K0629 42
10000.7
29,113111
LO000.53
59.603000
1O0AL03
DZASIS 36
LOWOL.L
1,00090
10000.79
1,000U
373222336 56
10000.76
59, 24
L.00U.4
56521129
10000.99
S. 75/288
L,000.00
CIDNrI-Aig
63,7825.02
64.7031.10
6311899777
67.12/3614
6177757.54
667533585
62.
62.7292.71
65
24/33.0
63,6/577600
649333.15
64.199355
1,000USS B5
LODROUS
1 1900399
CIDNee-Sitg
6867728010
LO009.94
6X 54
100KL97
LO005,96
GARDESSA 46
1,000.96
658506 26
LO000.93
GATNSS 70
1,000.86
L0000.9
1,00,.6
1 10000.39
64.7815 5V
LO00,92
CIDN
6139330.1
62.96/32.06
60.99141.77
65
61,2633691
63,87/36499
60.32/3322
63 63.8833500
61,1433850
62.52/33.43
62.A3355
1050085
10009.94
1000097
1
1.000
0100.99
1,000186
1,00096
10000.9
1.000091
CIDNaL-Wig
62,28225 MN
10050.86
23.56/34.29
1.000,95
61 01.42461,88
1.001197
1,002.96
1,00A197
OA 0IU3T.10
1.000,94
10
1,00/UST
MOXSLL1
66 1839666
10000.5
62/00/8
1,000.66
1,000.90
71/33
1,00/,93
81
Simulation Dataset. We adopt two widely used hyperspectral datasets: CAVE [1] and KAIST 6
The CAVE dataset contains 32 hyperspectral images with spatial resolution of 512 x 512. The KAIST dataset provides 30 high-resolution hyperspectral scenes of size 2704 x 3376. We obtain ground-truth speltil chromaticity and intensity using thromaticity-intennii decomposition Eq. (88). Following prior works [20 31 we use all CAVE images for training and select 10 scenes from KAIST for evaluation.
Implementation Details. Our model is implemented in PyTorch and trained using the Adam optimizer 113 for 300 epochs. The initial learning rate is set to 4 x 10-1 and updated using a cosine annealing schedule. We employ the &g loss between the reconstructed chromaticity and ground-truth chromaticity as the objective function. For training. we randomly extract 3D hyperspectral patches from each scene. For simulated data, the patch size is 256 x 256 x 28, for real-world data, we use patches of size 350 260 x 26. In simulation experiments, the forward imaging model is configured with a dispersion shift step d = 2, directing dispersion along the horizontal axis (rightward). In real-world scenarios, we assume a vertical dispersion direction and set d = 1, consistent with the dual-camera hardware setup. All experiments are conducted in Nvidia A40 GPU.
Comparing Methods. We compared the HSIS and chromaticity reconstruc- tion performance of our CIDNet with other 7 SOTA methods, including DeSCI171 GA.-Net[9]. MST-LBI. DAUHST-95tS 9stgg 4), SSR-9stg 38) and two dual-camera CASSI algorithm: PIDS[5] and In2SET-9stg (7)) PIDS is compared with RGB image as guidance (in original paper). We evaluate the reconstruction performance from two perspectives: hyper- spectral images (HSIs) and chromaticity. Unlike the baseline methods that directly reconstruct HSIs, our method assumes that the intensity is known and focuses on reconstructing the chromaticity. For a fair comparison in the HSI domain, we Figure 6: Real-data reconstruction in dual-camera CASSI. obtain our reconstructed HSIs by multiplying the recovered chromaticity with the known intensity. Conversely, for chromaticity-level comparison, we perform chromaticity-intensi decomposition on the HSIs reconstructed by the baseline methods to extract their chromaticity components. The reconstruction quality of HSIs is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
PIDS
Nirva
Nirua
Ririy
Ninta
CID-TV
Ninia
Nircfz
Ninaaa
Ninra
CIDNet
Ninia
Ninta
Nina
Nirva
463 mm
503 mm
587 nm
$15 m
8

---

Table 4: Break-down ablation study on individual components of the proposed method.
Base-
Int.
HSST
DNEM
PSNR
SSIM
Params
FLOPs
35.77
0.949
1.11
16.13
4
40.83
0.984
1.11
16.13
7
42.30
0.988
1.44
25.04
42.51
0.989
1.40
24.80
Table 5: Break-down ablation study on spectral self-attention mechanism.
Method
Basc-2
WSSA-WSSA
TKSA-TKSA
LWSA+TKSA
LWSA-TKSA
PSNR
40.95
41.98
42.05
42.36
42.51
SSIM
0.985
0.988
0.988
0,988
0,989
Params 1.12
1.33
1.33
1.27
1.40
FLOPs
18.03
23.29
25.16
23.47
24.80
Table 6: Break-down ablation study on intensity-guided mask.
Method
ADMM
ADMM-Int
MST
MST-Int
DAUHST
DAUHST-Int
PSNR
24.80
37.09 0.965
34.26
37.28
37.21 0.959
42.64
SSIM
0.712
0.935
0.971
0.989
3.1 Quantitative Results
As shown in Tab. 2 we compare the PSNR and SSIM of HSIs with SOTA methods. our proposed CIDNet excels in 8 out of 10 scenes, particularly in CIDNet-9stg, achieving an average PSNR of 44. 12dB and SSIM of 0.991. This significantly surpasses previous unfolding and end-to-end networks, and also in dual-camera CASSI systems, such as In2SET-9stg. A visual comparison is shown in Fig. 3 We provide the visual comparison of simulation Scene7 with 4 out of 28 spectral channels. In addition, we plot the spectral density curves of two regions in the top left RGB image. Our CIDNet-9stg achieves relatively higher spectral accuracy with reference spectra, demonstrating the effectiveness of our method. To verify the reconstructed quality of chromaticity and intensity, we compare the PSNR and SSIM of reconstructed chromaticity and intensity (with decomposition of HSIs), which is shown in 3 Note that our method assumes a known intensity. Therefore, to ensure a fair comparison of intensity, we decompose the reconstructed HSIs to extract their intensity component for evaluation. Our CIDNet achieves significant improvements in metrics of chromaticity and intensity. For dual-camera CASSI algorithm PIDS and In2SET, we achieve the best chromaticity metrics with PSNR of 35.81 1 and SSIM of 0.93. A visual comparison of reconstructed chromaticity shown in Fig. 4 In this research, we used a real-world DCCHI measurement Ninja, taken from publicly available data detailed in [9]. Fig. Gillustrates the reconstruction results for four spectral bands in this scene, using two dual-camera CASSI reconstruction algorithms, PIDS and CID-TV, where CID-TV is the iterative CID algorithm using Total Variationa Regularizer, details can be found in supplement materials. The comparison highlights the superior image restoration quality of model over other methods, validating its effectiveness and reliability in real-worle applications.
3.2 Ablation Study
Effectiveness of Intensity, HSST and DNEM. We verify the effectiveness of our proposed intensity- guided mask and two network module, HSST and DNEM. We adopt Base-1, derived by retaining binary mask and removing spatial-spectral attention and noise estimation from CIDNet-3stg to conduct the ablation study, where we used ground-truth HSIs instead of chromaticity for supervision. Tab, Tab.4]show shows the results of PSNR and SSIM of different settings, and our method achieves a significant 6.74dB PSNR improvements compared with Base-
Robustness of intensity-guided mask. We test the robustness of intensity-guider mask M' by employing this intensity mask to ADMM, DAUHST and MST, the result is shown in Tab. 6 We compare the reconstruction quality with/without sivensity-guider mask and find that it is significantly improving the base (without intensity) in iterative, end-to-end and unfolding framework. Note that DAUHST achieves slightly higher metric than ours. However its flops and parameters are also greater.
Self-attention scheme comparison. We compare Window-based Spectral Self-Attention (WSSA) 38 and spectral TKSA and its variants within the encoder and decoder. We use 't' to signify

---

parallel implementation of both attentions (each processing half of the feature channels), and to represent an encoder-decoder implementation. Base-2 CIDNet-3stg that removes the attention module. Tab. [ shows the ablation results and LWSA-TKSA yields the most prominent improvement of 1,56dB PSNR compared with Base-2, which shows the effectiveness of our method.
Conclusion
We present CIDNet, a novel reconstruction framework for CASSI, which leverages a physically motivated chromaaiccty-inteesiyy decomposition. By disentangling the hyperspectral image into spatially smooth intensity map and spectrally informative chromaticity cube, our method enables lighting-invariant reflectance modeling and better preserves spatial-spectral details. We have designed hybrid spatial-spectral Transformer to recover the sparse and high-frequency chromaticity compo- nents and introduced a degradation-awarr unfolding strategy with spatially adaptive noise modeling to handle anisotropic noise inherent in the dual-camera CASSI system. Extensive experiments on both simulated and real-world CASSI datasets validate the effectiveness of our approach, achieving state-of- the-art performance in spectral reconstruction and chromaticity fidelity. This work highlights the benefit of physics- aware decomposition and hybrid attention mechanisms in addressing the ill-posed inverse problem of CASSI reconstruction.
Acknowledgments
This work was supported by National Key R&D Program of China (2024YFF0505603) the National Natural Science Foundation of China (grant number 6227 1414), Zhejiang Provincial Distinguished Young Scientist Foundation (grant number LR23F010001 Zhejiang 'Pioneer' and 'Leading Goose' R&D Program (grant number 2024SDXHDX0006, 2024C03182 the Key Project of Westlake Institute for Optoelectronics (grant number 2023GD007), the 2023 International Sci-tech Cooperation Projects under the purview of the 'Innovation Yongjiang 2035' Key R&D Program (grant number 2024Z126), National Natural Science Foundation of China (grant number 62501487). (Corresponding author: Xin Yuan.)
References
[1] Gonzalo R Arce, David Brady, Lawrence Carin, Henry Arguello, and David S Kittle. Com- pressive coded aperture spectral imaging: An introduction. IEEE Signal Processing Magazine, 31(1):105-115, 2013.
[2] Sean Bell, Kavita Bala, and Noah Snavely. Intrinsic images in the wild. ACM Transactions on Graphics (TOG), 33(4):1-12, 2014.
[3] Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van Gool. Mask- spectral-wise transformer for efficient hyperspectral image reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and patlern recognition, pages 17511-17511, 2022.
[4] Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Henghui Ding, Yulun Zhang, Radu Timofte, and Luc Gool. Degradation-awarc unfolding half-shuffle transformer for spectral compressive imaging. Advances in Neural Information Processing Systems, 35:37749-37761. 2022.
[5] Yurong Chen, Yaonan Wang, and Hui Zhang. Prior image guided snapshot compressive spectral imaging. IEEE Transactions Pattern Analysis and Machine Intelligence, 4509):1106--1117,, 2023.
[6] Inchang Choi, Daniel Jeon, Giljoo Nam, Diego Gutiérrez, and Min Kim. High-quality hyper- spectral reconstruction using spectral prior. ACM Transactions Graphics, 36:1-13, 11 2017.
[7] Yubo Dong, Dahua Gao, Tian Qiu, Yuyan Li, Minxi Yang, and Guangming Shi. Residual degradation learning unfolding framework with mixing priors across spectral and spatial for compressive spectral imaging. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 2222-22277 2023.
10

---

[8] Elena Garces, Carlos Rodriguez-Pardee Dan Casas, and Jorge Lopez-Moree Sopez-Moreno. A survey on intrinsic images: Delving deep into lambert and beyond. International Journal of Computer Vision, 130(3):836-866) 2022.
[9] Wei He, Naoto Yokoya, and Xin Yuan. Fast hyperspectral image recovery of dual-camera compressive hyperspectral imaging via non-iterative subspace-based fusion. IEEF Transactions on Image Processing, 30:7170-7183, 2021.
[10] Xiaowan Hu, Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Lue Van Gool. Hdnet: High-resolution dual-domain learning for spectral compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17542-17551, 2022.
[11) Xudong Jin, Yanfeng Gu, and Tianzhu Liu. Intrinsic image recovery from remote sensing hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 57(11:224-233 2018.
[12] Xudong Kang, Shutao Li, Leyuan Fang, and Jón Atli Benediktsson. Intrinsic image decompo- sition for feature extraction of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(4422241-2253, 2014.
[13] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6988 2014.
[14] Miaoyu Li, Ying Fu, Ji Liu, and Yulun Zhang. Pixel adaptive deep unfolding transformer for hyperspectral image reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12959-12968, 2023.
[15] Zhengqi Li and Noah Snavely. Cgintrinsics: Better intrinsic image decomposition through physically-based rendering. In Proceedings of the European conference on computer vision (ECCV), pages 371-387, 2018
[16] Zhengqi Li and Noah Snavely. Learning intrinsic image decomposition from watching the world. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9039-9048, 2018.
[17] Yang Liu, Xin Yuan, Jinli Suo, David J Brady, and Qionghai Dai. Rank minimization for snapshot compressive imaging, IEEE transactions on puttern analysis and machine intelligence, 41(12):2990-0006 2018.
[18] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference computer vision, pages 10012-10022, 2021.
[19] Ziyi Meng, Shirin Jalali, and Xin Yuan. Gap-net for snapshot compressive imaging. arXiv preprint arXiv:2012.08364 2020.
[20] Ziyi Meng, Jiawei Ma, and Xin Yuan. End-to-end low cost compressive spectral imaging with spatial-spectral self-attention. In European conference computer vision, pages 187-204. Springer, 2020.
[21] Xin Miao, Xin Yuan, Yunchen Pu, and Vassilis Athitsos. I-net: Reconstruct hyperspectral images from a snapshot measurement. Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4059-4069, 2019.
[22] Zhenghao Pan, Haijin Zeng, Jiezhang Cao, Kai Zhang, and Yongyong Chen. Diffsci: Zero-shot snapshot compressive imaging via iterative spectral diffusion model. In Proceedings of the IFEF/CVF Conference on Computer Vision and Pattern Recognition, pages 25297-25306, 2024.
[23] Mengjie Qin, Yuchao Feng Zongliang Wu, Yulun Zhang, and Xin Yuan. Detail matters: Mamba-inspired joint unfolding network for snapshot spectral compressive imaging. arXiv preprint arXiv:2501.01262 2025.
11

---

[24] Haiquan Qiu, Yao Wang, and Deyu Meng. Effective snapshot compressive-spectral imaging via deep denoising and total variation priors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9127-9136, 2021.
[25]
Ashwin Wagadarikar, Renu John, Rebecca Willett, and David Brady, Single disperser design for coded aperture snapshot spectral imaging. Applied optics, 47(10):B44 B51, 2008.
[26] Jiamian Wang, Kunpeng Li, Yulun Zhang, Xin Yuan, and Zhiqiang Tao. S 2-transformer for mask-aware hyperspectral image reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[27] Xin Wang, Lizhi Wang, Xiangtian Ma. Maoqing Zhang, Lin Zhu, and Hua Huang. In2set: intra-inter similarity exploiting transformer for dual-camera compressive hyperspectral imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24881 128911, 2024.
[28] Chen Wei, Wenjing Wang. Wenhan Yang. and Jiaying Liu. Deep retinex decomposition for low-light enhancement. arXiv preprint 1rXiv:1808 008.00560. 2018.
[29] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex- net: Retinex-batt based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5901-5910, 2022.
[30] Zongliang Wu, Ruiying Lu, Ying Fu, and Xin Yuan. Latent diffusion prior enhanced deep unfolding for snapshot spectral compressive imaging. In European Conference on Computer Vision, pages 164-181. Springer, 2024.
[31] Fumihito Yasuma, Tomoo Mitsunaga, Daisuke Iso, and Shree Nayar. Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum. IEEE transactions on image processing, 19(9):2241-23)
2010.
[32] Wenbo Yu, Lianru Gao, He Huang. Yi Shen, and Gangxiang Shen. Hi 2 2 fnet: Hyper- spectral intrinsic image decomposition guided data fusion network for hyperspectral and lidar classification. IEEE Transactions on Geoscience and Remote Sensing, 61:1-15, 2023.
[33] Xin Yuan. Generalized alternating projection based total variation minimization for compressive sensing. In 2016 IEEE International conference on image processing (ICIP), pages 2539-2543, IEEE, 2016.
[34] Xin Yuan, David J Brady, and Aggelos K Katsaggelos. Snapshot compressive imaging: Theory, algorithms, and applications. IEEE Signal Processing Magazine, 38(2):65-88 88, 2021.
[35] Haijin Zeng, Benteng Sun, Yongyong Chen, Jingyong Su, and Yong Xu. Spectral compressive imaging via unmixing-driven subspace diffusion refinement. In The Thirteenth International Conference on Learning Representations.
[36] Jian Zhang and Bernard Ghanem. Ista-net: Interpretable optimization inspiired deep network for image compressive sensing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1828-1837, 2018.
[37] Jiancheng Zhang, Haijin Zeng, Jiezhang Cao, Yongyong Chen, Dengxiu Yu, and Yin-Ping Zhao. Dual prior unfolding for snapshot compressive imaging. In Proceedings of the IEEF/CV Conference on Computer Vision and Pattern Recognition, pages 25742 2024.
[38] Jiancheng Zhang, Haijin Zeng, Yongyong Chen, Dengxiu Yu, and Yin-Ping Zhao. Improving spectral snapshot reconstruction with spectral-spatial rectification. In Proceedings of the IEEF/CVF Conference Computer Vision and Puttern Recognition, pages 25817-2588 2024.
[39] Siming Zheng, Yang Liu, Ziyi Meng, Mu Qiao, Zhishen Tong, Xiaoyu Yang, Shensheng Han, and Xin Yuan. Deep plug-and and-play priors for spectral snapshot compressive imaging. Photonics Research, 9())B18 B-B29, 2021.
12

---

NeurIPS Paper Checklist
Claims
Question:
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
We clearly state that we focus on improving the hyperspectral reconstruction com-
munity.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contribu- tions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
It is fine include aspirational goals as motivation as long as it clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We describe the limitations of our work in Appendix.
Guidelines
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proof's
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification: We give the complete proofs of our theoretical results in Appendix ??.
13

---

Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper the supplemental material, but if they appear the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proof' provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main experi- mental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification:
The experimental results can be reproduced by the updated source code.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided not.
the contribution a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to hosted model (e.g., in the case of large language model), releasing of model checkpoint, or other means that are appropriate to the research performed.
Depending the contribution, reproducibility
While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution primarily new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is new model (e.g., large language model), then there should either be way access this model for reproducing the results or way reproduce the model (e.g.. with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-souree models, it may be that access to the model is limited in some way (e.g.. to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification: Link to GitHub will be available upon release of the paper and the source code is zipped aside in the supplementary material.
Guidelines:
14

---

The answer NA means that paper does not include experiments requiring code.
Please the NeurlPS code and data submission guidelines (https::/nnips.cc/pllll)
guides/Com Ctth//ooeeSSuiciceee isimiissoon assionnoicyy for more details.
While we encourage the release of code and data, we understand that this might not be possible,
so "No" is an acceptable answer. Papers cannot rejected simply for not including code, unless this is central to the contribution (c.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed reproduce the results. See the NeurIPS code and data submission guidelines (https for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
The detailed experimental setting is shown in Section
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification:
For all algorithms, each task runs 10 instances with different random seeds.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to library function, bootstrap, etc.)
The assumptions made should be given (c.g., Normally distributed errors).
It should be clear whether the error bar the standard deviation the standard error of the mean.
It is OK to report 1-sigma bars, but should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (c.g. negative error rates).
15

---

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed reproduce the experi- ments?
Answer: [Yes]
Justification:
The computer resources are shown Section
S
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experi- mental runs well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: httpaaaaii//.iassuuudiiiiii..aaa
Answer: [Yes]
Justification: We have reviewed the NeurlPS Code of Ethics. The research conducted in the paper conforms, in every respect, with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.
The authors should make due to laws or regulations in their jurisdiction).
a preserve anonymity (e.g., if there is special consideration
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Justification:
We do not foresee any societal impact of our work.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (c.g.. disinformation, generating fake profiles, surveillance), fairness considerations (c.g.. deploy- ment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to par- ticular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
16

---

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g,, gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of MI.).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, scraped datasets)?
Question:
Answer: [NA]
Justification:
We do not foresee any risk for misuse in our work.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions access the model implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging. and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
12. Licenses for existing assets
Question: Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer:
[Yes]
Justification:
We are under licenses of MIT, BSD, and the Python software foundation.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version the asset used and, possible, include a URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset. For scraped data from particular source (e.g., website), the copyright and terms of service of that source should be provided.
assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, papprssw thcode. cww cwm/dataanac datasett has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged reach out the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
The paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release assets.
Researchers should communicate the details of the dataset/codemmodel as part of their sub- missions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
17

---

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text instructions given to participants and screenshots, applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on requirements of your country or institution) were obtained?
Answer: answerNA
Justification:
The paper does not involve crowdsourcing nor research with human subjects
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code Ethics and the guidelines for their institution.
For initial submissions, do include any information that would break anonymity (if applica- ble), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: The core method development in this research does not involve LLMs as any important, original, non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, or non-standard components.
what Please refer to our LLM policy hattee//paarrpppccc///oarrrrerrp should or should not be described.
18

---

A Appendix
In the supplementary material, we provide more details that are not in out main paper:
(a) Multispectral Image Formation in Sec.
A.I
(b) Proof of Illumination- Inviriinnnnn-aniieeeeeeeeeeeee of Chromaticity in Sec. A.2
(c) Proof of PAN-Intensity Equivalence in Sec. A.3
We demonstrate the PAN image in dual-camera
CASSI is equivalent to the Intensity image.
(d) Closed-form Solution of Data-fidelity Term in Sec.
A.4
(e) Traditional Optimization- Methods in Sec. A.5
Some results are visualized.
(f) Visual Comparison of HSIs and Chromaticity in Sec.
A.6
g Noise Map Visualization of DNEM in Sec. A.7
(h) Limitation and Broader Impact of Our Work in Sec.
A.8
A.1 Multispectral Image Formation
Let (u, v) be spatial coordinates and E [Amin: Amax] the wavelength. Assume the scene reflectance is R(u, v, (this is not the same as data-prior term), and the illumination spectral power is L()), spatially uniform. Let s()) be the spectral response function of the grayscale camera, which converts the spectral radiance to scalar intensity. Then the intensity recorded by the grayscale camera at (u, v) is:
Amiu
I(u, r)
(2)-L(A R(u, v. v,
dx. d)
(25)
Amtn
This is the measurement observe from the grayscale camera. We define multispectral distribution:
X(u, u, = s()) L(A) R(u, A)
(26)
Then the multispectral chromaticity function is defined as the normalized form of X(u, v, A) over À
Clu, v, = X(u, v, = J s() L(A) R(u, U. A) I(u,v)
s(A) L(A) R(u, u, u, A)
(27)
which leads the final formulation of spectral product of intensity and chromaticity:
X(u, v, C(u, v, A) @ I(u.v)
(28)
Next we demonstrate that the chromaticity is illumination invariant, and the intensity can be obtained via dual-camera setting.
A.2 Proof of Illumination-Invarinn of Chromaticity
Now suppose the illumination changes globally:
L(A) + a. L(A), a> 0
Then:
s(A) aL(A): R(u, C'(u,v,)) J s(X) aTT()) RRu,, v,) dx s(A) L(A) RR(,v,
(29)
((()) -(X) R(u, u, U, X) = v)
(30)
19

---

Thus, the chromaticity C(u, U, A) is invariant to uniform intensity changes in illumination, even when considering the grayscale camera's 's spectral sensitivity.
A.3 Proof of PAN-Intensity Equivalence
Proposition A.1 (PAN-Intensity Equivalence Under Uniform Illumination). In dual-camera system comprising a CASSI sensor and grayscale PAN camera exposed under the same illumination L(X), let s(X) denote the spectral response of the camera. The PAN image IPAN(U, v) provides relative estimate of the scene intensity [(u, v) defined by the chromaticit-iitensiiy decomposition of the hyperspectral image X(u, U. X). That is,
IPAN (u, k- I(u,)),
(31)
where k is scalar constant that approximately invariant across spatial coordinates (u, v).
Proof. Our derivation begins with the Retinex theory, which decomposes an image into chromaticity and intensity components. For hyperspectral images, this decomposition is generalized as:
((u, v, A) C(u,u,,) I(u, v),
(32)
where the chromaticity and intensity are defined as:
X(u,v,
Clu, 1 X(u,v, u. N,XX
(33)
I(u,v) X(u,e,
(34)
In our dual-camera setup with CASSI and PAN sensors under identical illumination L(A), the PAN image formation is modeled as:
IAN (u, v) = 5(A) L(A) X(u, v, A)dX.
(35)
Substituting the X into the product of chromaticity and intensity yields:
IPAN(U, s(A)T())(C(,,, A) I(u, v)]d2.
(36)
Since I(u, v) is independent of wavelength X, it can be factored out of the integral:
IAAN (u, v) I(u,v) v) f ((X)L())C((, A)dA.
(37)
The key insight is that C(u, v, A) is normalized (ʃ C(u, v, X')X' = 1) and exhibits smooth spectral variation, while (A) acts as broadband low-pass filter. This justifies the approximation:
(A)T()))( v, A)dA R k,
(38)
where & spatial-invariant scalar constant. Therefore, we obtain:
IPAN (u, v) k- I(u,
(39)
To address scale ambiguity, we normalize the PAN image [0, 1] during training and inference, which justifies using PAN as relative intensity estimate in our chromaticity-inteet framework. D
20

---

A.4
Closed-form Solution of Data-fidelity Term
The c-subproblem in Eq. (3 quadratic and has closed-form solution:
(k+1) (H T p1) Σ- + uzz(h)
(40)
Note that H E-'H is a fat matrix and (H - + vd) will be difficult to compute and thus we simplify it based on the Sherman-Morrisee Morrison-Woodburyy formula,
H
H
pl) - - - " (E+
(I+ HH)
H.
(41)
By plugging Eq.
41 into Eq.
(40)
we formulate it as
H y + pz (k)
Σ + p
HH
y
H E + p
Hz
(k++1)
1
12
(42)
In CASSI systems, HH is diagonal matrix defined HH diag(of...... o d.) we obtain:
Blag(hi..... hn With E &
Σ n ( HHH) diag
1
(43)
For hn
n + hn S
phj
phn
Σ + HH
HH = diag
(44)
[oo]
hi
fo + hn
Let y
.y..... ynl JT and [H denote the i-th element (k(k), We plug Eq. 42 as
(43) and Eq.
(44)
into Eq.
41h1 + [HH fo7 + /11 yn
yalh + p ()(He!)))
(k++1) =1 'H
+ (k) - 1 HT
(45)
1O07 + hn
= (k) + y1 [H] 1 + hn
(H)
(46)
fo + hn
Generally this is a generalized form of gradient descent, which is expressed as,
(k++)) (k) HHH + ++E) ty (z)
(47)
A.5 Traditional Optimization-basee based Methods
We explore two traditional pptimization-based paradigms considering PAN-guided and RGB-guided intensity respectively, where we formulate the optimization problem using TV prior as,
è argmin 1 lly (c o i)ll + +TV(c).
(48)
where is the sensing matrix determined by the modulation and dispersion process, e and are chromaticity and intensity respectively, the noise estimation term is omitted since it is hard to be estimated iterative methods. consider dual-camera setting where the second camera could be grayscale or RGB camera, which satisfy i = :PAN ;RGB In RGB scenarios, the RGB RGB-guided intensity is three-channel image, which cannot multiplied directly with & due to the channel mismatch. Hence, we interpolate the RGB image to the same spectral channels with corresponding HSIs. Using the HQS framework. Eq. (88 is minimized by solving the following subproblems iteratively by introducing z:
(k+1) argming Hlyy
Hc 1 + f1lc- - (2)) 1| TTV(2),
(49)
(k+1) argmin,
(50)
Following previous derivation Eq 49 and traditional TV denoising term Eq 50 we iterate these two steps to approach its finest solution. The reconstruction is compared with iterative methods such as GAP-TV, DeSCI and PIDS and presented in Fig Scene5 is selected for better visulization purpose.
21

---

AU8
GA29 MussRTTI
0.5-
-P 05-8GB
CadCi own 0307
SIG
SIG
SIG
SIG
DHRK sinee
0---
1
0:4
Sit
SIt
SIG
SIG
02-
400
800
5E0
903
690
Wavelength (nm)
Spectral Density Curve
GAP-TV
DeSCI
PIDS-GB
CID-PAN (Oun)
CID RGB (Qum)
Figure 7: Reconstructed HSIs using traditional optimization based methods.
A.6 Visual Comparison of HSIs and Chromaticity
We compare the HSIs and chromaticity using KAIST datasets. As shown in Fig. select out of 10 test datasets, with the top row being the RGB reference and intensity image. Four spectral images selected for visulization. It can be seen from the figure that chromaticity enhance the low-light regions while preserving more spatial textures as compared with regular spectral images, demonstrating that chromaticity contains more features.
A.7 Noise Map
In this paper we propose dual noise estimation module for data-fidelity term and denoising network. This network module designed to estimate a spatially adaptive noise map E, two noise map output corresponding to the gradient projection noise map and the proximal mapping noise map respectively, where we use convolution and channel attention (CA) to enhances informative channels by modeling inter- channel relationships, as shown in Fig. This structure guarantees positivity and adaptiveness of the output noise map, suitable for uncertainty modeling or variance-art aware image restoration tasks. We conduct the experiment and find in CIDNet-3stg, the gradient projection noise map is prominent in the 1st stage and fades away the latter 2 stages. While the proximal mapping noise map exhibits finer structure in the 2nd and 3rd stages. This is reasonable since more uncertainty is present in the 1st stage introduced by the noisy measurement while the denosing network focus more on the residual learning.
3x3 Conv
1X1 Conv
3x3 Conv
Softplus
CA
RaLU
3x3
Figure 8: The network structure of step map estimation.
A.8 Limitation and Broader Impact of Our Work
Our work assumes pre- measured intensity and utilizes grayscale or RGB image obtained by dual-camera CASSI system to serve as an intensity image. This is how we obtain intensity image and also our limitation. However, we expect that this decomposition framework is applicable to regular CASSI system. By obtaining the intensity image though a regular CASSI training and then freeze this intensity network, continue training the CIDNet for chromaticity reconstruction. This could be further explored our future work. Moreover, our coammaticit--nntensee decomposition framework opens a new paradigm for low-light or shadow-remore hyper-spectral reconstruction since the chromaticity represents more abundant scene/sample information.
22

---

Scene01
Scene06
Scana07
Scenc088
DCT
536
0ed Dmn
HSI
Chromaticity
Figure 9: Comparison between HSTs and chromaticity on KAIST dataset.
Stage
Stage
Stage
Projection
Mapping Map
Figure 10: Visulization of dual noise estimation module in CIDNet-3stg.
23


Reviews:
[{"review_id": "oxzim3frf7", "reviewer": "Reviewer_M6fb", "summary": "The manuscript proposed a solution for CASSI image reconstruction.\nMain novelties could include:\n1. Separates the chromaticity-intensity map of an image\n2. Models the noise covariance and denoising strength with a neural network\n3. Top-K spectral attention for reducing the computational overhead.", "questions": "Is prox and proj implemented as the proposed transformers?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. A dedicated network structure that reaches SATO in the datasets.\n2. Cascade network architectures that realize the functionality of an optimization scheme. \n3. The plots and equations are generally informative.\n\nWeakness:\n1. Some equations are confusing; the positive/negative signs and the brackets in Eqs 10, 11, 12 need to be checked. \n2. LWSA TKSA are not explained, and it is not clear what has been compared in the ablation.\n\nIt is generally good work, I will accept the publication once the authors clarify the unclear points and correct the figures and formulas in the manuscript.", "was_revised": true, "final_justification": "The authors have answered my questions, hence I will keep my previous positive recommendation.", "rebuttal": "Thanks for your recognition and valuable comments, we address your concerns one by one.\n\n> **Q1-1: Some equations are confusing, signs and brackets need to be checked.**\n\nA1-1: Thanks for your kindly suggestions. We will carefully correct the writting errors in the final manuscript, especially your mentioned\n\n$\\hat{\\mathbf{c}} = \\arg min_{\\mathbf{c}} \\left(-\\frac{1}{2} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})^\\top \\boldsymbol{\\Sigma}^{-1} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})\\right) + \\tau R(\\mathbf{c})$\n\n$\\textstyle \\hat{\\mathbf{c}} = \\arg min_{\\mathbf{c}, \\mathbf{z}} \\left(-\\frac{1}{2} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})^\\top \\boldsymbol{\\Sigma}^{-1} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})\\right) + \\tau R(\\mathbf{z}), \\quad \\text{s.t.} \\quad \\mathbf{c} = \\mathbf{z}. $\n\nwould be corrected as\n\n$\\hat{\\mathbf{c}} = \\arg min_{\\mathbf{c}} \\left(\\frac{1}{2} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})^\\top \\boldsymbol{\\Sigma}^{-1} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})\\right) + \\tau R(\\mathbf{c})$\n\n$\\textstyle \\mathbf{\\hat{c}},\\mathbf{\\hat{z}}  = \\arg min_{\\mathbf{c}, \\mathbf{z}} \\left(-\\frac{1}{2} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})^\\top \\boldsymbol{\\Sigma}^{-1} (\\mathbf{y} - \\mathbf{H}\\mathbf{c})\\right) + \\tau R(\\mathbf{z}), \\quad \\text{s.t.} \\quad \\mathbf{c} = \\mathbf{z}.$\n\n> **Q1-2: LWSA and TKSA are not explained.**\n\nA1-2: We provide motivation and technical details of our hybrid transformer module: LWSA (Local Window Spatial Attention) and TKSA (Top-K Spectral Attention).\n\n*Motivation*:\nChromaticity exhibits rich high-frequency details in the spatial domain (see Figure 1), but its spectral signatures tend to be sparse and low-rank (see Figure 5). To effectively model this structure, we use LWSA (a window-based spatial attention) to capture local spatial textures. We design a Top-K Spectral Attention (TKSA) to focus on the most informative spectral dimensions for each spatial location. While LWSA is a typical window transformer, we focus on the explanation of TKSA.\n\n*Techinical details of TKSA*:\nTop-K Spectral Attention (TKSA): Instead of applying global attention across all spectral bands—which is both computationally expensive and often dominated by noisy or redundant information—we propose to:\n- Divide the image into non-overlapping spatial windows.\n- For each window, we first calculate the attention map in the spectral dimension, this attention map size is C×C, for each line in the attention map, we keep the maximum K value, and the other values are set to -∞, so that after the softmax operation, these values will become 0, and we finally use this attention map to weight the key and get the output.\n\n> **Q1-3: Ablation study explanation.**\n\nA1-3:  Below, we clarify the motivation and significance of each experiment by highlighting the core novelties of our work. Our main contribution is to reformulate spectral reconstruction as a chromaticity prediction problem by decomposing the spectral image into intensity × chromaticity. The intensity is directly obtained from the PAN image captured by the dual-camera CASSI system, while chromaticity contains the essential spectral variation. Based on this, we design targeted modules for effective chromaticity reconstruction.\n\n- **Table 3** presents the contribution of three components:\n\n(1) Int.: our decomposition-based framework that incorporates PAN-guided intensity;\n\n(2) HSST: a hybrid spatial-spectral transformer tailored for local spatial and sparse spectral patterns in chromaticity;\n\n(3) DNEM: a dual-noise estimation module that models spatially-varying degradation across unfolding stages.\n\n- **Table 4** compares different attention types within the encoder-decoder. The hybrid spatial-spectral attention (TopK) yields the best performance, validating the importance of our design.\n- **Table 5** further verifies that our intensity-chromaticity decomposition generalizes well across different types of frameworks (iterative, end-to-end, and unfolding), demonstrating its broad applicability and core novelty.\n\n> **Q1-4: Is prox and proj implemented as the proposed transformers?**\n\nUnfolding network typically alternates between a gradient projection and a proximal mapping step to enforce measurement consistency and data prior updating respectively. Hereby we use $\\mathbf{proj}$ and $\\mathbf{prox}$ to denote the gradient projection and proximal mapping respectively, where $\\mathbf{proj}$ is some linear operators and $\\mathbf{prox}$ contains a learnable network with our hybrid transformer."}, {"review_id": "JaIUKOt905", "reviewer": "Reviewer_Ka4a", "summary": "The paper proposes CIDNet for dual-camera compressive hyperspectral imaging. It factors the HSI cube into a panchromatic-derived intensity map and a chromaticity tensor, then embeds the intensity into the forward model via an intensity-weighted CASSI mask. Reconstruction is carried out by a nine-stage unfolded solver that alternates physics-based gradient steps with an asymmetric hybrid transformer, while a dual noise-estimation block adaptively tunes both the gradient term and the denoiser. On CAVE, KAIST, and real data, CIDNet surpasses recent transformer baselines in PSNR/SSIM with fewer parameters, and ablations confirm the benefit of each module.", "questions": "1.The paper criticises prior work for conflating illumination with reflectance, yet in a dual-camera setting the PAN and CASSI sensors run independent auto-exposure, so differences in shutter time and ISO mean their irradiances are on distinct scales. Because the PAN gray image is used verbatim as the intensity map I(x,y) in the chromaticity–intensity decomposition—without any radiometric calibration or learnable scale factor—might the resulting exposure mismatch simply be absorbed into the chromaticity term C(x,y,λ), thereby recreating the very illumination-reflectance entanglement the manuscript seeks to avoid?\n\n2.In the Dual Noise-Estimation Module (Eqs. 19–20) a single lightweight CNN outputs both the gradient-projection covariance Σ(k) and the denoising weight ω(k), yet the manuscript neither (i) explains how Σ(k) is constrained to remain positive-definite nor (ii) analyses possible gradient interference between the two predictions. Could the authors therefore clarify (1) which activation or re-parameterisation (e.g., soft-plus, exponential) is used to ensure every entry of Σ(k) is strictly positive and the associated matrix inversion remains numerically stable, and (2) whether the norms/variances of Σ(k) and ω(k) remain well-behaved throughout training; additionally, if the two quantities are predicted by separate branches or one is fixed (e.g., Σ(k)=σ²I), how do PSNR/SSIM and convergence speed respond? \n\n3.The paper mainly compares CIDNet with single-camera baselines such as DeSCI and MST-L; to make the evaluation complete and fair, please include (i) those single-camera models adapted to the dual-camera setting and/or (ii) additional methods originally des.\n\n4.The manuscript claims that Chromaticity-Intensity decomposition outperforms plain end-to-end 3-D convolution, yet Table 3 compares only the Base-1 model (no attention) with versions that add the proposed modules. Please include an additional baseline that directly predicts the HSI cube without C-I decomposition while still using HSST and DNEM, so we can verify that the reported gains are attributable to the decomposition itself rather than to the attention architecture.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: the method integrates a physically motivated chromaticity-intensity decomposition and an intensity-weighted mask, couples them with a well-structured nine-stage unfolded solver, introduces an efficient hybrid attention (window-spatial + sparse spectral) backbone, and adds a dual noise-estimation block, yielding state-of-the-art PSNR/SSIM on CAVE, KAIST and two real scenes with fewer parameters than recent transformer baselines. \nWeaknesses: the PAN-derived intensity prior is used without exposure/ISO calibration, the jointly regressed Σ and ω lack positive-definite constraints and stability evidence, real-scene evaluation is minimal, strong dual-camera baselines are absent, ablations are coarse (Top-K size, stage sharing, fixed vs. learned Σ/ω, etc.).", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your recognition and valuable comments, we address your concerns one by one.\n\n> **Q2-1: Exposure mismatch in dual-camera chromaticity-intensity decomposition.**\n\nA2-1: This is a valid concern. However, in practice, we apply normalization to the intensity map to mitigate exposure differences both during training and testing. During training (simulated data), the intensity map is normalized by averaging across all spectral channels: $I(x, y) = \\frac{1}{L} \\sum_{\\lambda=1}^L X(x, y, \\lambda)$. During real testing (dual-camera system), we normalize the captured PAN image by dividing by its maximum value: $I_{\\text{norm}}(x, y) = \\frac{I_{\\text{PAN}}(x, y)}{\\max(I_{\\text{PAN}})}$.\n\nAs a result, both the intensity and chromaticity maps are treated as relative quantities. While the absolute radiometric scale may be lost, the relative spatial distribution of intensity is preserved, which is sufficient to support meaningful chromaticity estimation and reconstruction. Moreover this formulation is widely accepted in related literature on (RGB) low-light imaging and intrinsic image decomposition, where illumination is often treated as a relative quantity and gamma correction is used to simulate different lighting conditions (see [1,2]). For example, we can enhance the perceived brightness via: $I_{\\text{gamma}}(x, y) = I_{\\text{norm}}(x, y)^{\\gamma}, \\quad \\gamma \\in (0.5, 2.0)$.\n\n\n\n> **Q2-2: Concerns about Dual Noise-Estimation Module (DNEM).**\n\nA2-2: We thank the reviewer for the interest in Dual Noise-Estimation Module (DNEM). We explain the concerns in two aspects.\n\n(1) Positive-definiteness of $\\Sigma^{(k)}$.\n    We argue that $\\Sigma^{(k)}$ is not a full covariance matrix but a *diagonal positive definite matrix* representing spatially varying noise variance (see [3]). Specifically, $\\Sigma^{(k)} = \\operatorname{diag}(\\sigma_1^{2}, \\dots, \\sigma_M^{2})$. To ensure all entries are strictly positive and avoid numerical instability during inversion, we apply the Softplus activation to the output of the CNN branch predicting log-variances and add a small constant $\\varepsilon$ for numerical stability: $\\sigma_i^{2} = \\log(1 + \\exp(\\hat{\\sigma}_i)) + \\varepsilon$. where $\\hat{\\sigma}_i$ is the raw output from the network. This guarantees each entry in $\\Sigma^{(k)}$ remains positive-definite by design.\n\n(2) Gradient interference and stability analysis.\n    We employ a shared CNN encoder, followed by two lightweight separate heads to predict $\\Sigma^{(k)}$ and ω(k), respectively. This modular design alleviates potential gradient interference. Results (Table 3 and SM Figure 4) show that using learned $\\Sigma^{(k)}$ improves reconstruction PSNR and SSIM, highlighting the importance of noise-adaptive spatial modeling in CASSI. We will revise the manuscript to clearly state that $\\Sigma^{(k)}$ is a diagonal variance map and provide details about softplus activation and the dual-head structure to improve clarity.\n\n> **Q2-3: Single-camera models adapted to the dual-camera setting.**\n\nA2-3: In the original submission, we compared our proposed CIDNet with In2SET (CVPR-2024) and PIDS (TPAMI-2023), which is the state-of-the-art method for dual-camera compressive hyperspectral reconstruction. Our results showed that CIDNet outperforms In2SET and PIDS significantly in simulated and real scenarios. To futher demonstrate the effectivesss of our decomposition method, we have also adapted popular single-camera reconstruction models (unfolding framework GAP-Net [4] and an E2E model MST [5]) to the dual-camera setting. The detailed results are now summarized as follows. The expriments are compared with 9-stage unfolding framework and it is shown that with Chroma-Intensity Decomposition greatly improve the reconstruction quality. \n\n|              | GAP-net-9stg | GAP-net-9stg w/ CI | MST-L | MST-L w/ CI | CID-Net-9stg w/ CI|\n| ------------- | ------------ | --------------- | ----- | -------- | ------------ |\n| Avg PSNR (dB) | 33.26        | 38.62           | 35.18 | 39.22    | 44.12    |\n| Avg SSIM      | 0.917        | 0.977           | 0.948 | 0.980    | 0.991    |\n\n> **Q2-4: Additional baseline that directly predicts the HSI cube without Chro-Intensity Decomposition.**\n\nA2-4: We have added a new baseline that directly predicts the HSI cube without Chro-Intensity Decomposition. The performance of CIDNet improved significantly after adding C-I decomposition. We compared the MST [5] with our CID-Net. At the same time, without C-I decomposition (i.e., direct reconstructing hyperspectral images without input PAN images), our network still has better performance with lower parameters and FLOPs. \n\n|               | MST-S | CID-Net-3stg w/o CI | CID-Net-3stg w/ CI |\n| ------------- | ----- | -------------------- | ------------------ |\n| Avg PSNR (dB) | 34.26  | 36.85                | 42.51          |\n| Avg SSIM      | 0.935  | 0.961                | 0.980          |\n| Params (M)    | 2.03    | 1.40           | 1.40        |\n| FLOPs (G)     | 28.15  | 24.80            | 24.80         |\n\n**References:**\n\n[1] Chen, Chen, et al. \"Learning to See in the Dark.\" *CVPR*, 2018.\n\n[2] Bell, Sean, et al. \"Intrinsic images in the wild.\" *ACM TOG*, 2014.\n\n[3] Glaubitz, Jan, et al. \"Generalized sparse Bayesian learning and application to image reconstruction.\" SIAM/ASA Journal on Uncertainty Quantification 11.1 (2023): 262-284.\n\n[4] Meng, et al. \"Gap-net for snapshot compressive imaging\".  *arXiv *, 2020.\n\n[5] Cai, et al. \"Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition\".  *CVPR*, 2022."}, {"review_id": "pqAGU107Vj", "reviewer": "Reviewer_DTCy", "summary": "The paper proposes a new method called CIDNet, which revolutionizes dual-camera CASSI reconstruction by splitting images into intensity and chromaticity components, enabling robust spectral recovery under varying light. Its hybrid Transformer architecture captures rich textures and sparse spectral features, while adaptive noise modeling handles complex degradation.", "questions": "see weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The chromaticity-intensity decomposition leverages the physical properties of light and reflectance, leading to a more robust and interpretable reconstruction.\n\n2. The experiments demonstrate that CIDNet achieves good performance.\n\nWeaknesses:\n\n1. The authors claimed that the intensity image can be approximated as a PAN image (line 96) without specific explanations. Is there any theory that can support this point? \n\n2. This work integrates some network modules from other works, such as top K attention, noise estimation, etc. The contributions of these are very limited but have brought almost all the improvements to the network. The core of this work is the estimation of Chromaticity-Intensity. By contrast, the gain it brings is very limited.\n\n3. The mark that appears after the word 'appendix', for example, lines 62 and 101. Please carefully check the manuscript and correct similar mistakes.", "was_revised": true, "final_justification": "The author's reply solved some problems, so I will adjust the score. However, it is not appropriate to compare the methods of different camera systems, so I can still only give the borderline score.", "rebuttal": "Thanks for your valuable comments, we address your concerns one by one.\n\n> **Q3-1: On the justification of using PAN image as intensity.**\n\nA3-1: The idea of using PAN image as intensity is inspired by *Retinex theory* [1] widely used for low-light image enhancement, where a RGB image is decomposesed into reflectance and illumination. This is typically formulated as:  \n$$\n\\mathbf{X}(u,v) = \\mathbf{C}(u,v) \\cdot \\mathbf{I}(u,v)\n$$\nwhere $\\mathbf{C}$ denotes chromaticity (color) and $\\mathbf{I}$ denotes intensity (illumination). We generalize this concept to hyperspectral images as:\n$$\n\\mathbf{X}(u,v,\\lambda) = \\mathbf{C}(u,v,\\lambda) \\cdot \\mathbf{I}(u,v),\n$$\nwhere chromaticity and intensity are defined as:\n$$\n\\mathbf{C}(u,v,\\lambda) = \\frac{\\mathbf{X}(u,v,\\lambda)}{\\int \\mathbf{X}(u,v,\\lambda') d\\lambda'}, \\quad\n\\mathbf{I}(u,v) = \\int \\mathbf{X}(u,v,\\lambda) d\\lambda.\n$$\nIn a dual-camera setup with a CASSI sensor and a grayscale PAN camera, both exposed under the same illumination $ L(\\lambda) $, $s(\\lambda)$ is the spectral camera response. we model the PAN image as the integral of three terms:\n\n$$\n\\mathbf{I}_{\\text{PAN}}(u,v) = \\int s(\\lambda) \\cdot L(\\lambda) \\cdot \\mathbf{X}(u,v,\\lambda) \\cdot d\\lambda.\n$$\n\nSubstituting the decomposition of $ \\mathbf{X} $ , we obtain:\n\n$$\n\\mathbf{I}_{\\text{PAN}}(u,v) = \\mathbf{I}(u,v) \\cdot \\int s(\\lambda) L(\\lambda) \\mathbf{C}(u,v,\\lambda) \\, d\\lambda.\n$$\n\nThe key observation is that $\\mathbf{C}(u,v,\\lambda)$ is normalized (the integral of $\\mathbf{C}(u,v,\\lambda)$ over wavelength equals 1) and typically smooth or slowly varying in $\\lambda$, while $s(\\lambda)L(\\lambda)$ acts as a broadband low-pass kernel. This enables the approximation:\n$$\n\\int s(\\lambda) L(\\lambda) \\mathbf{C}(u,v,\\lambda) d\\lambda \\approx k,\n$$\nwhere $k$ is a scalar constant across spatial positions. Thus, we have:\n$$\n\\mathbf{I}_{\\text{PAN}}(u,v) \\approx k \\cdot \\mathbf{I}(u,v).\n$$\n\nTo resolve the scale ambiguity, we normalize PAN to [0,1] during both training and inference. This justifies using PAN as a relative intensity estimate in our chromaticity–intensity framework, and we will add this proof into our supplement.\n\n> **Q3-2: Concerns on network module design and the gain of chromatic-intensity decomposition.**\n\nA3-2:  **This decomposition is the core contribution** of our paper and brings several conceptual and practical advantages that go beyond simple numerical gains in PSNR or SSIM. We demonstrate this in two aspects:\n\n(1) This decomposition allows us to separate illumination material-dependent reflectance (chromaticity) and intensity from single measurement, providing a representation that is more physically meaningful and invariant to lighting changes. We believe this will significantly benefit CASSI-based reconstruction and its downstream applications such as remote sensing, water quality detection, or hyperspectral material classification, where chromaticity serves as a more precise fingerprint of material properties.\n \n(2) Secondly, by explicitly modeling intensity, we enable applications that target illumination manipulation. For instance, we can apply gamma correction to the intensity map to achieve low-light enhancement, which would be infeasible if illumination and reflectance were entangled.\n\nAbout the network design, although our network components (e.g., spatial–spectral transformers) are not entirely novel, their design is carefully adapted to suit the properties of chromaticity (sparse spectra and high-frequency details). We also propose a dual noise estimation module to handle anisotropic noise across different reconstruction stages. This is backed by theoretical modeling and empirical evidence, and improves convergence and visual quality.\n\nThe gains of this decomposition is our main contribution, which is manifest in two aspects: spectral reconstruction (Table 1) and chromaticity reconstruction (Table 2). Ours outperform single-camera (DAUHST[NeurIPS 2022], SSR[CVPR 2024]) and dual-camera schemes (PIDS[TPAMI 2023], In2SET[CVPR 2024]).\n\nIn summary, our goal is not only to improve metrics but to reformulate the problem in a more interpretable and physically grounded way, which we believe is a valuable step for the CASSI research community.\n\n> **Q3-3: Manuscript check.**\n>\nA3-3: Thanks for the suggestion, we will correct the errors accordingly.\n\n\n**References:**\n\n[1] Wei et al., *Deep Retinex Decomposition for Low-Light Enhancement*, BMVC 2018."}, {"review_id": "Mg9es6JVLV", "reviewer": "Reviewer_3wmZ", "summary": "This paper describes a novel, interpretable, physically-based method for recovering chromaticity information from compressive spectral measurements taken with the CASSI architecture. Defining hyperspectral chromaticity as the normalized per-pixel spectrum in a hyperspectral image and assuming a known normalizing intensity (e.g., from a second camera), the method modifies the CASSI forward model and poses a regularized optimization problem in chromaticity. This problem is solved with a deep unfolded network to maintain interpretability, and the regularizer is incorporated with alternating optimization. A compact noise estimation model is used to set the noise level for each step to guide the optimization. The regularizer used is a novel hybrid spatial-spectral transformer that preserves high-frequency spatial detail while encouraging sparse local dependencies in the spectral domain. The results show superior performance metrics in chromaticity reconstruction than existing methods on simulated data, and clearer chromaticity reconstruction in one real-world result.", "questions": "Please see the weaknesses section. Specifically, please address the following:\n- utility of chromaticity in downstream tasks where intensity is also important\n- how would real-world intensity image estimation artifacts affect the reconstruction\n- metrics in noisy versions of the simulated CASSI data\n- the best and worst-performing metrics for the 30 images in the test dataset\n- the choice of the ten test images\n- best results not highlighted in bold in Table 1", "limitations": "Only one limitation—about the requirement for an intensity image—is described. Please see the weaknesses section for more. Also please move it into the text of the main paper because it is a very significant assumption.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The method is interpretable via its unfolding structure.\n- The HQS approach allows for independent control over the data fidelity and regularization terms.\n- The dual-noise estimation framework is interesting in giving the network a map of the uncertainty to tune each unfolded step update.\n- In estimating chromaticity, CIDNet seems to perform better than the other existing methods on the 10 images taken for evaluation in simulation and the single real-world image.\n\nWeaknesses:\n- The utility of the chromaticity-intensity decomposition is unclear to me. Color information encoded in chromaticity is an important lighting-invariant part of the spectral characteristics of an object. However, the intensity component is important as well for further tasks such as mixed-material analysis, material identification and surgical guidance. So why not reconstruct the hyperspectral cube and then, if needed, decompose it?\n- As for the dependence on lighting spectrum and intensity: that can be calibrated out with a spectrally-flat target. For downstream tasks that require intensity, even though reconstructing chromaticity with CIDNet is lighting-independent, getting intensity independent of lighting still requires this calibration.\n- Supplement Figure 3 shows the difference between chromaticity and HSI to make the point that chromaticity enhances the signal in the image. However, it also decreases contrast between different materials along the intensity axis, and in real-world settings, would potentially amplify noise in low-signal regions. \n- Estimating the intensity image separately has the potential to introduce artifacts—for instance, in occlusion and misalignment in dual-camera setups, and smearing or blurring in the PAN image. How do these affect the reconstruction? Since in the simulated CASSI measurements in the results, the intensity image is taken as the ground truth is not really “dual-camera”.\n- Has there been any noise introduced in the simulation to emulate the effects of real-world conditions and get performance metrics?\n- There is only one real-world result shown with a dual-camera setup which seems to be a planar scene, therefore not having any of the problems in the dual-camera setup above. Additionally, I do not believe that performing better on one scene adequately validates the real-world superiority of CIDNet.\n- Only 10 out of the 30 images in the KAIST dataset were picked for evaluation. How were they picked? Are the average metrics in the last column shown only on these 10? What are the best- and worst-performing metrics for all 30 images? This will help mitigate concerns of selection bias.\n- Table 1: In scenes 1, 3, 5, 8, 10, no CIDNet is the best result in either PSNR or SSIM. Please explain why CIDNet is still highlighted as the best result.\n\nWriting fixes:\n- Please cite sources for these: (1) line 96, PAN images can be used as intensity, (2) line 130, HQS.\n- Here are some suggestions: (1) Figure 1 caption “Chrmaticity”->”Chromaticity”, (2) Figure 2: seems like the labels (b) and (c) are flipped, (3) line 63: “global”->”spatial”, (4) line 127:  unused Mahalanobis norm definition, (5) line 134: “fat matrix”->”wide matrix”, (6) line 247: repeated “the chromaticity”, (7) supplement line 2: “Multispectral”->”Hyperspectral”. \n- Please fix the cross-references wherever they say “??”", "was_revised": true, "final_justification": "The authors' responses answered my questions satisfactorily, so I will move my rating up.", "rebuttal": "Thanks for your valuable comments, we address your concerns one by one.\n\n> **Q4-1: Utility of chromaticity in downstream tasks where intensity is also important. So why not reconstruct the hyperspectral cube and then, if needed, decompose it?**\n\nA4-1: We agree that both chromaticity and intensity are important for downstream applications such as material identification and surgical guidance. However, in this work, we specifically focus on chromaticity reconstruction, due to the following considerations:\n\n(1) Task difficulty and reconstruction fidelity.\n    Chromaticity encodes fine-grained texture and material information and is generally harder to reconstruct, whereas intensity primarily reflects low-frequency lighting patterns, which are relatively easier to recover. Therefore, we directly supervise and reconstruct the chromaticity component, which proves to be more effective than reconstructing the full hyperspectral cube followed by decomposition (see Table 2). The direct supervision helps preserve high-frequency chromatic information crucial for tasks like object fingerprinting.\n\n(2) Joint ambiguity in decomposition\n    Jointly reconstructing both chromaticity and intensity introduces ambiguity, especially in regions where the product of the two can yield similar measurements but from different chromaticity-intensity combinations. Our current pipeline does not explicitly resolve this ambiguity. However, we observe that a stage-wise strategy—where the first stage reconstructs intensity (with frozen weights) and the subsequent stages focus on chromaticity—still achieves competitive performance. This code will be released.\n\n> **Q4-2: Utility of chromaticity in downstream tasks where intensity is also important, still needs calibration.**\n\nA4-2: We agree that absolute chromaticity and illumination intensity can be obtained through pre-calibration using a spectrally-flat target. In fact, we also plan to incorporate this calibration-based strategy in future work to estimate physically accurate lighting conditions. However, in our current experiments, we adopt a normalized dual-path PAN image as a proxy for intensity. This is not a physically absolute lighting measurement, but a relative intensity estimate. This choice is motivated by two practical considerations:\n\n(1) Stability during training.\n    Using normalized intensity improves data consistency and helps stabilize model training, especially under variations in exposure, gain, and scene content.\n\n(2) Focus on spatial variation rather than absolute magnitude.\n    Our aim is to leverage the spatial structure of intensity changes (e.g., shading, highlights) rather than the absolute illumination levels. The relative intensity still captures rich cues about lighting distribution, which is sufficient for our chromaticity reconstruction pipeline.\n\n> **Q4-3: About noise amplification in dark region.**\n\nA4-3: This is a valid and commonly recognized issue in chromaticity-based representations, especially in low-light or shadowed regions where signal-to-noise ratio is inherently low. Such issue are also extensively discussed in the Retinex and intrinsic image literature (see [1] and [2]). In our case, we agree that this effect exists and is hard to eliminate completely. There are two practical strategies to mitigate it:\n\n(1) Retraining on real-world datasets containing paired chromaticity-intensity hyperspectral data under diverse lighting conditions. This would allow the model to learn better noise-aware priors in low-light regions. Unfortunately, such datasets are currently unavailable to the best of our knowledge.\n\n(2) Post-processing on the reconstructed chromaticity, such as applying TV-denoising or bilateral filtering to suppress artifacts in dark areas. While feasible, this approach introduces additional hyperparameters and lacks ground-truth supervision, making quantitative evaluation difficult.\n\n> **Q4-4: Real-world experiment difficulties due to misalignment.**\n\nA4-4: In our current real-world experiments, however, we use publicly available real CASSI system datasets. That said, we fully acknowledge that in a true dual-camera configuration, such issues can impact the quality of the estimated intensity image, and in turn affect chromaticity reconstruction. We will consider the suggestions.\n\n> **Q4-5: Noise simulation in simulation and real-data condition.**\n\nA4-5: In simulation experiment, both CASSI measurements and the PAN images are noise-free (for fair comparison with the baseline algorithm). In real experiment, follow the setting of TSA-Net [3], we injected 11-bit, quantum efficiency 0.4 shot noise into both CASSI measurements and PAN images.\n\n> **Q4-6: About adding more real-world dataset testing.**\n\nA4-6: We agree that validation on more complex real scenes is important. Due to platform and cost constraints, we have not yet been able to collect a large-scale real dataset. To date, the only publicly available dual‑camera compressive hyperspectral imaging (DCCHI) dataset appears in the paper [4], which provides exactly two real‑scene captures: one ninja scene and one doll scene.\n\n> **Q4-7: About KAIST dataset selection.**\n\nA4-7: The KAIST benchmark for spectral imaging was first proposed in TSA-Net [3], these 10 scenes are randomly chosen, and this benchmark was used in a series of subsequent papers focus on spectral reconstruction like MST[CVPR 2022], DAUHST[NeurIPS 2022], SSR[CVPR 2024], In2SET[CVPR 2024].\n\n> **Q4-8: Table 1 not highlighted best result.**\n\nA4-8: Thank you for pointing this out. We sincerely apologize for the confusion. Few metrics in one baseline work PIDS [5] are incorrect, causing the misunderstanding.  The reconstructed results are available in the original benchmark from [5] (which is a dual-camera CASSI baseline), and we have now revised the table accordingly. By the way, to illustrate the effectiveness of our Chroma-Intensity Decomposition method. We adapted PIDS into PIDS-CIDS, where we formulate the optimizationproblem as：\n$$\n 𝑐̂ = argmin_c (1/2)‖y − Φ(c ⊙ i′)‖² + τ·TV(c − i'),  s.t.  i' = interpolate(i_{rgb})，\n$$\n\nwhere TV denotes Total Variation and $\\text{interpolate}(\\mathbf{i}_\\text{rgb})$ means to interpolate the RGB image captured by the second camera to spectral channels (28 channels in our case). This is to align with the PIDS setting (the second camera is RGB camera).\n\n| Method      | S1                   | S2                   | S3                   | S4                   | S5                   | S6                   | S7                   | S8                   | S9                   | S10                  | Avg                  |\n| ----------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- |\n| PIDS        | 42.09  0.983         | 40.08  0.949         | 41.50  0.968         | 48.55  0.989         | 40.05  0.982         | 39.00  0.974         | 36.63  0.940         | 37.02  0.948         | 38.82  0.953         | 38.64  0.980         | 40.24  0.967         |\n| PIDS-CIDS | 41.02  0.983         | 43.19  0.983         | 40.93  0.930   | 49.23  0.988 | 38.53  0.983         | 39.41  0.978         | 37.49  0.960         | 37.86  0.972         | 39.35  0.965    | 39.06  0.984 | 40.61  0.973         |\n\nIt can be found that PIDS-CID better results compared with pure original one. Some visilization results are presented in supplement Figure 7 (denoted as PIDS-RGB).\n\n\n> **Q4-9: Suggestions on Writing fixes.**\n\nA4-9: Thank you for pointing this out. We will correct the writing accordingly.\n\n\n\n**References:**\n\n[1] Wei et al., *Deep Retinex Decomposition for Low-Light Enhancement*, BMVC 2018.\n\n[2] Bell, Sean, et al., *Intrinsic images in the wild*,  ACM TOG, 2014.\n\n[3] Meng, et al., *End-to-end low cost compressive spectral imaging with spatial-spectral self-attention*, ECCV, 2020.\n\n[4] Zhang, et al., *Fast hyperspectral image recovery of dual-camera compressive hyperspectral imaging via non-iterative subspace-based fusion*, IEEE TIP, 2021.\n\n[5] Chen et al., *Prior image guided snapshot compressive spectral imaging*,  IEEE TPAMI 2023."}]

Author rebuttal:

Meta-review: The submission introduces a new framework for dual-camera compressive hyperspectral imaging. All reviewers recommended acceptance of the submission, and found the rebuttal to have mostly addressed their concerns. The area chair agrees with the reviewers' consensus, and strongly recommends the authors to incorporate the rebuttal and discussions in the camera ready.
Decision: Accept (poster)