Title: Spectral Compressive Imaging via Chromaticity-Intensity Decomposition
Venue: NeurIPS 2025
Accepted: yes
Keywords: computational imaging

computational photography
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}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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 “??”\n\nQUESTIONS:\nPlease 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\n\nLIMITATIONS:\nOnly 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-22"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

1. The chromaticity-intensity decomposition leverages the physical properties of light and reflectance, leading to a more robust and interpretable reconstruction.

2. The experiments demonstrate that CIDNet achieves good performance.

Weaknesses:

1. 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? 

2. 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.

3. The mark that appears after the word 'appendix', for example, lines 62 and 101. Please carefully check the manuscript and correct similar mistakes.

QUESTIONS:
see weakness.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: 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.).\n\nQUESTIONS:\n1.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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
The manuscript proposed a solution for CASSI image reconstruction.
Main novelties could include:
1. Separates the chromaticity-intensity map of an image
2. Models the noise covariance and denoising strength with a neural network
3. Top-K spectral attention for reducing the computational overhead.

STRENGTHS AND WEAKNESSES:
Strength:
1. A dedicated network structure that reaches SATO in the datasets.
2. Cascade network architectures that realize the functionality of an optimization scheme. 
3. The plots and equations are generally informative.

Weakness:
1. Some equations are confusing; the positive/negative signs and the brackets in Eqs 10, 11, 12 need to be checked. 
2. LWSA TKSA are not explained, and it is not clear what has been compared in the ablation.

It 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.

QUESTIONS:
Is prox and proj implemented as the proposed transformers?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 1
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01