Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Authors: Zhongnan Cai
Yingying Wang
Hui Zheng
Panwang Pan
ZiXu Lin
Ge Meng
Chenxin Li
Chunming He
Jiaxin Xie
Yunlong Lin
Junbin Lu
Yue Huang
Xinghao Ding
Venue: NeurIPS 2025 oral ()
Keywords: Look-Up Table
Pan-sharpening
Primary area: deep_learning

Abstract: Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicability of these methods in real-world scenarios, particularly in the absence of dedicated computing devices such as GPUs and TPUs. To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for large remote sensing images. Our method makes it possible to process 15K$\times$15K remote sensing images on a 24GB GPU. To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral mapping. To effectively capture fine-grained spatial details, we introduce the spatial details look-up table (SDLUT). Furthermore, to adaptively aggregate channel information for generating high-resolution multispectral images, we design an adaptive output look-up table (AOLUT). Our model contains fewer than 700K parameters and processes a 9K$\times$9K image in under 1 ms using one RTX 2080 Ti GPU, demonstrating significantly faster performance compared to other methods. Experiments reveal that Pan-LUT efficiently processes large remote sensing images in a lightweight manner, bridging the gap to real-world applications. Furthermore, our model surpasses SOTA methods in full-resolution scenes under real-world conditions, highlighting its effectiveness and efficiency. We also extend our method to general image fusion tasks.

Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Zhongnan Cail* Yingying Wang'*
Hui Zheng' Panwang Pan Chunming He
ZiXu Lin'
Ge Meng' Yunlong Lin't Junbin Lu Yue Huang' Xinghao Ding
Chenxin Li
Jiaxin Xie
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, Fujian, China ByteDannce 'The Chinese University of Hong Kong Duke University University of Washington
Abstract
Recently, deep learning-basse pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicability of these methods in real-world scenarios, particularly in the absence of dedicated comput- ing devices such GPUs and TPUs. To address these challenges, propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes balance between performance and computational efficiency for large remote sensing images. Our method makes it possible to process 15K x 15K remote sensing images a 24GB GPU. To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral map- ping. To effectively capture fine-grained spatial details, introduce the spatial details look-up table (SDLUT). Furthermore, to adaptively aggregate channel information for generating high-resolution multispectral images, we design an adaptive output look-up table (AOLUT). Our model contains fewer than 700K parameters and processes 9K*9K image in under ms using one RTX 2080 Ti GPU, demonstrating significantly faster performance compared to other meth- ods. Experiments reveal that Pan-LUT efficiently processes large remote sensing images in a lightweight manner, bridging the gap to real-world applications. Fur- thermore, our model surpasses SOTA methods in full-resolution scenes under real-world conditions, highlighting its effectiveness and efficiency. We also ex- tend our method to general image fusion tasks. The source code available at https::/githhb.. cot. httgg//wwww.oo///////gaa///e//ea
Introduction
High-resolution multispectral (HRMS) images are widely used in applications such military operations, environmental monitoring, and mapping. However, due to the limitations of physical sensors, these images challenging to obtain. Pan-sharpening addresses this issue by fusing high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images,
producing high-quality HRMS images through complementary integration (55) 61 42 Recently,
numerous pan-sharpening methods have been proposed, which can be generally categorized into
"Equal Contribution.
Project Leader.
t Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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18000
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PNN
MSDCNN
GPPNN
GCPNet
spanConv
LGPCOnV
PGCU
UAPN-5
PSCINN
Mamba
DIFHOP
3698.51
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Figure 1:
Comparisons of computational efficiency. Our method can process 9K*9K and
15K x 15K images on GPUs with 11GB and 24GB memory, respectively. Meanwhile, we observe that (a) DNN-based methods are highly sensitive to the image size, and (b) in the absence of GPU, they require a considerable amount of time to process images. In the CPU inference time experiments, all methods were conducted on a workstation equipped with an Intel(R) Xeon(R) Gold 6226R CPU.
two main groups: traditional methods and deep learning-basce methods. Traditional methods, such as component substitution (CS) 22
multi-resolution analysis (MRA) 19 and variational optimization (VO) [10 35, often struggle to restore precise spatial or spectral details in HRMS images. In contrast, deep learning- baased nan-sharpening methods have demonstrated exceptional fusion capabilities due to the powerful feature extraction ability of deep neural networks (DNNs) 23
24 27 (88) Masi et al. [33] built the Pan-sharpening Neural Network (PNN) model, which first applied CNN to pan-sharpening field, achieving significant improvement over traditional methods. Following this, researchers have explored more complicated and deeper networks to further
promote the performance of pan-sharppe sharpening [13] 25
(34] (56). However, they overlook critical practical issue: the need for real-time processing of large remote sensing images in real-world applications. As illustrated in Figure 1 we observe
major limitations in DNN- based approaches:
(1) they are highly sensitive to the size of the input images, and (2) they rely heavily on dedicated computing devices such as GPUs and TPUs. Increasing GPU memory does not significantly improve the image size these methods can handle and several of these methods demand a significant amount of time to process images in CPU-only environments. And will demonstrate in the experiments sections that, even with GPU acceleration, most methods still fail to process large remote sensing images in real time. In practical applications, remote sensing images typically exhibit even higher resolutions, posing additional challenges to existing methods in terms of efficiency and scalability. Moreover, simply increasing network depth does not necessarily lead better performance, as deeper models are harder to train and often suffer from overfitting due to redundant parameters.
To overcome the aforementioned challenges, we propose novel learnable Look-Up Up Table (LUT)) framework, called Pan-LUT, which achieves a good balance between performance and computational efficiency in pan-sharp.ning. Specifically, LUT's to enable lightweight deployment in practical applications. To finely control the spectral transformation, devise the PAN-guided look-up table (PGL.T)) for channel-wise spectral mapping. To effectively capture fine-grained spatial details, we introduce the spatial details look-up table (SDLUT). To further enable adaptive channel aggregation for high-resolution multispectral image generation, we design the adaptive output look-up table (AOLUT). The Pan-LUT consists of fewer than 700K parameters and can process 9K x9K images under I ms using a single RTX 2080 Ti GPU, Furthermore, our approach outperforms traditional methods by 7 dB, while maintaining a speed comparable to that of conventional techniques, demonstrating superior speed and efficiency compared to existing methods. Our contributions can be summarized as follows:
replace complex DNN operations with learnable
We present Pan-LUT, a novel learnable LUT framework that does not incorporate any network structure. This framework designed to achieve a strong balance between per- formance and computational efficiency in pan-sharpening high-resolution remote sensing

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images. Our method makes it possible to process 15Kx 15K remote sensing images on one 24GB GPU.
To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral mapping. To effectively capture fine-grained spatial details and adaptively learn local contexts, we introduce the spatial details look-up table (SDLUT). To further enable adaptive channel aggregation for high-resolution multispectral image generation, we design the adaptive output look-up table (AOLUT).
To the best of knowledge, this is the first attempt introduce LUTs for efficient pan-sharpening. Extensive experiments on different satellite datasets demonstrate the effectiveness and efficiency of Pan-LUT.
2 Related Work
2.1 Look-Up Table
Look-up Tables (LUT)) are particularly useful for functions of multiple variables, as they store pre- computed outputs for all possible input combinations. For example, in 1D LUT, single input index mapped an output value, often using linear interpolation for indices that fall between pre-stored values. More complex LUTs, such as 3D LUTs, use three independent input variables, which may require advanced interpolation methods like trilinear or tetrahedral interpolation. Due to its portability,
various LUT based solutions have been proposed for image enhancement [6] [22] [26
[39 [52]
For instance, Zeng et al. [52] and Wang et al. [39] propose image-adaptive 3D LUTSs for efficient single-image enhancement. These approaches rely on network weight predictor to fuse different
3D LUTs, which may pose a limitation on platforms under essoucce-constrane
conditions. Addi-
tionally, LUT-based methods have been explored in the area of super-resolution [18] [1] 31 [29].
SRLUT 18 trains a deep super-resolution (SR) network with a restricted receptive field and then caches the output values from the learned SR network in LUTss However, issues such performance degradation arise when large patches are cached in LUTs, prompting the development of strategies like MuLUT [21], which introduces multiple LUT variants and fine-tuning strategy improve performance. To further enhance the functionality of LUTs, architectures like SPLUT (31) and RCLUT [29] have been proposed. SPLUT processes different image information separately using multiple LUTs, while RCLUT introduces a plugin module to improve LUT-bbaed models with minimal additional computational cost.
2.2 Traditional Pan-sharpening Methods
Traditional fusion techniques encompass component substitution (CS), multi-resolution analysis
(MRA), and variational optimization (VO). CS methods, such IHS 2 Brovey (11. and PCA (.) utilize spatial details from high-resolution panchromatic (PAN) images to replace corresponding details in low-resolution multispectral (LRMS) images, which can lead spectral distortion due to the incomplete incorporation of spectral information. MRA techniques, including DWT [19] and ATWT (7]. apply multi-resolltion decomposition to merge PAN and LRMS images, which enables better preservation of spectral information and reduces spectral distortion. VO methods, such as Bayesian 10] and Total Variation (35), formulate the fusion process as an optimization problem by iteratively minimizing the loss function. While VO methods show promising results, they encounter challenges in optimizing model design and loss functions. Although these approaches have yielded certain improvements, their performance remains constrained by the inadequate modeling, which restricts further advancements in pan-sharpening accuracy and quality.
2.3 Deep Learning-basee based Methods
Deep learning- based methods have emerged the dominant approach for pan-sharpening in recent years [40
[4 43 The PNN (33) model, inspired by SRCNN [8) is the first to introduce CNNs into this domain, surpassing traditional methods. Models like PanNet 44 and MSDCNN [50] further enhance performance by leveraging residual connections and multi-scale convolutions, effectively capturing high-frequency details and supporting a wide range of remote sensing applications. Since then, more complex CNN-based architectures 4 [15] 62) have been proposed in this field to improve the mapping ability of pan- sharpennnnning Models like GPPNN [45), MMNet [47], and ARFNet [46).
3

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Overall framework of PAN-LUT
Spectral Transformation
Details of PGLUT
Final
Ploats input without PAV GuidGddddd
Poals outpur without PAN Guided
Concalenation
PGLUT
20
25
20
home
28
24
25
26
Non
LOOK UP
20
home
24
25
26
None
Upsampled
Identicall plelcare
without Hovi-aee
Mlenticall pladi rean acoucitr plae inedinopping with suited
Pleels input with PAN- MAN-Guided
Plee's output with FAN-Guided
20
29
27
29
25
20
Spatial Details Transformation
Adaptive Output
SDLUT
For each Channel Look UP
ruppoette ctoneey foo candders
AOLUT
segaboemod
Look UP
Indix
(% Fa.
HRMS
Darrent plae! and Adjecent piaels
lodea
55,55,5,5
Figure 2: The overall framework of our proposed Pan-LUT. PGLUT is a spectral transforma- tion LUT designed to extract spectral information, SDLUT is spatial detail transformation LUT for capturing texture features, and AOLUT an adaptive output LUT used aggregate channel information.
which enhance interpretability through deep unfolding techniques and accelerate model convergence. Spatial adaptive convolution methods, such as LAGConv [17] and CANCouv [ can adaptively generate different convolution kernel parameters based various spatial locations, enabling them to accommodate different spatial regions. Other approaches, including SFDI 660 and MSDDN [50], utilize Fourier transforms to capture high-frequency features. Transformer- bosoomeer-bbeed architectures, such as INN-former [59], Panformer [57] and DRFormer [53] combine CNNs and Transformers to capture both local and global features. Instead of learning deterministic mapping, generative models such as UCGAN [58] PanFlow (48] and PSCINN [3 generate a distribution of possible outputs for the given inputs. In addition, several studies have investigated lightweight pan-sharpening methods, including SpanConv [5] and LGPCony [54]. However, we show that current lightweight methods only lightweight in the context of low-resolution images. Despite their promising results, these advanced methods come with high computational costs, limiting their practical applicability.
3
Method
Given the PAN image (P E R=*Wxx1) and the MS image (MS E R#/rxW//xX),, pan-sharpening aims to fuse the complementary information to generate the desirable high spatial resolution MS image (H RMS R#*Wxck Here, H and W denote the height and width of the images, represents the spatial resolution ratio, with value of 4, and denotes the number of spectral bands.
3.1 Framework
The overall framework our proposed Pan-LUT illustrated in Figure 2 which consists of three specifically designed LUTs. (1) To finely control the spectral transformation, we devise the PAN- guided look-up table (PGLUT) for channel-wise spectral mapping, which incorporates PAN-guided indexing strategy and a pentalinear interpolation technique. (2) To effectively capture fine-grained spatial details and adaptively learn local contexts, we introduce the spatial details look-up table (SDLUT), which incorporates rotation-enii enciineed indexing strategy and quadrilinear interpolation technique. (3) further enable adaptive channel aggregation for high-resolution multispectral image generation, we design the adaptive output look-up table (AOLUT), which incorporates PAN-guided indexing strategy and a pentalinear interpolation technique. Specifically, given the PAN image (P E Il Hx XX11 and the upsampled MS image (M S R fx W xC) they concatenated

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into PM E (X*WX(C+1) and passed through the PGLUT for channel-wise spectral mapping. producing the output Vig E [C+W) SDLUT then takes Vpg as input to generate local spatial details, yielding Ved E ( Finally, AOLUT takes Vnd as input to generate the final HRMS R result:
Pg = PPLUT(PM) Vad SDLUT(( HRMS AOLUT(V..)
(1)
3.2 Spectral Transformation
To preserve the rich spectral information of the MS image, we propose the PAN-guided look-up table (PGLUT), which leverages the PAN image guidance finely control the spectral transformation. Specifically, PGLUT is represented as 5-dimensional matrix containing No elements, where N denotes the number of bins per dimension. Each element corresponds a set of indexed input pixels (((.J.R,,,,))) O... and their corresponding output pixels , b, nér} represents the pixel values from the PAN and MS images, while E (R, G, B, NIR] denotes the corresponding cached output pixels. Since the LUT elements are discretely distributed in space, the output value cannot be directly retrieved from the LUT. For an input value (pa( " (w.,)) (o.n))((..)) ((w.)) nir((.,)) h, where (w, h)
sampling point, defining
{O m.n) N... Here, E (pa, r,
denotes the spatial position of pixel in the image.
PAN-guided indexing strategy. As illustrated in Figure 2 we introduce an indexing strategy for precise spectral transformation, referred to as the PAN-guided indexing strategy. a MS image, pixels from different spatial locations may have identical values (e.g.. T; rj = タ5 4; by när; = nirj. where * j). The LUT maps these identical inputs to the same output. The PAN-guided indexing strategy provides more flexible indexing mechanism for the LUT. Specifically, it additionally considers the pixels at corresponding spatial positions in the PAN image (c.g., r; = 9t = gy. b, = by. nir, nir,, pa, * paj. where & j). thereby achieving üiner-graine mapping. Specifically, PGLUT first performs a lookup operation to locate the corresponding input pixel in the LUT:
pu(
((wi.)
K(w,)) Vmaa
x
N.
.N,
N,
Viaa
Vinax
b/ (w.N)
nér (w:h)
Vmaz -N,==
Vmaa NN
where Vmaz denotes the maximum value (e.g., 255, 1023 or 2047). The coordinates of the sampling
points, L = {(i c,j + c, k + c, m c, + c)}, with c {0, }}. can be derived as follows:
i = [ yl. |z]. [s].
[e],
where [.] denotes the floor function. {d.).=r.y,.... represents the offset of the input index (x,, y, e, relative to the defined sampling point (1,j, k, m, n), e.g., d, = x - i.
Pentalinear Interpolation. After locating 32 adjacent points, an appropriate interpolation technique is applied to these sampled values to generate the output:
O (z.g.2,a.e) P Interpolalion (di)),
where PInterpolation(() denotes the pentalinear interpolation. More details about PGLUT and the pentalinear interpolation can be found in Section A.2
3.3 Spatial Details Transformation
PGLUT is essentially a channel-wise 5D LUT that operates globally, which limits its ability to capture local spatial information. To effectively capture fine-grained spatial details and adaptively learn local contexts, we propose the Spatial Details Lookup Table (SDLUT).
Rotation-indexing strategy. As illustrated in Figure 2 given a pixel P(w.A) SDLUT processes this pixel along with its neighboring pixels as input. During the training phase, we employ a Rotation-indexing strategy to further expand the receptive field, which can be formulated
Pl = fSDLUT P(w+ I) P( P( fSDLUT (p P P Pl P P(),
(5)
Plw.h) -SSmuT((i ( M PP P P(
V(w,N) [SDLLU((R D 1,)) (l
5

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Table I: Quantitative comparison across three satellite datasets. The best outcomes are highlighted in red. I indicates better performance with increasing values, while + signifies improved performance with decreasing values.
Method
WorldView-I1
GanFen2
Worldview-III
ParamuMM)
Inference (ms)
PSNRT
[SSIM]
[SAM]
ERGASS
PSNRT
[STM
[SAM]
|RGGAS]
PSNRT
[SIM]]
[AM]
ERGAS.
2R x 2X 48 x 4.
Reewyy
35,8646
1.9216
1.8238
57,2974
0.9026
[00218
1.3720
22.5060
0.5466
0.1159
8
0.28
0.33
IHS
35.2962
0.9027
B04461
2,0278
38,1754
0,9100
010243
1.5336
22,5579
0.5354
0.1266
8.3616
0.23
12.66
SFIM
34.1297
0.8975
0:0429
2.3449
36,9950
000315
1.7398
21.5212
0.5457
0.1208
8.730
0.32
0.47
GS
35.6376
0.9176
0.0423
1.8774
$7,2260
0,9034
0.00009
1.6736
22.5608
0.5477
0.1217
8.2433
0.75
0.87
PNN
40.7550
0.0259
43. 1200
0000172
29.94188
0.9121
0.0824
3,3206
12.11
PonNet
40.8176
0.9625
0/0055
1,0557
43.0659
0,0685
0.01778
0.8577
29 19.6840
0.9072
0.0851
3.4263
0.0688
29.52
0OM
MSDCNN
41.3355
0.9664
0.0042
0,9340
45.6847
010155
0.9184
0.0782
3.1854
0.2590
49.82
00M
Paz-GAN
39.0025
0.9562
0.0000
1,2954
41.4468
0.9661
000200
1.0093
28.4959
0.8897
0.0998
39007
0.0815
40.83
0OM
GPPNN
41.1622
0.9684
0,0244
1,0315
44.2145
0,9815
0.0135
0.7361
37.5
0.9175
0.0776
3,2593
0.1198
43.60
0OM
SEDI
41.7244
0.9725
0,0020
0,9506
47,4712
0.9901
0U01002
0.5462
30.5571
0.9256
0.0741
3.0798
0.0871
05:37
0OM
UOGAN
40.0545
0.9553
0,0275
1,1734
42.3634
0,9557
0.0194
0,9480
28.6705
0.8851
0,0950
3,8696
0,2109
52.06
0OM
PrnFlow
41,8584
(..7712
0.0124
0.9335
47.2533
0.9884
0.0103
0.5512
30.4873
0.9221
0.0751
3.1143
0.0873
54-19
DOM
PSCINN
41,8500
0,970
[000223
0.9407
47.1100
0,9878
0101077
0.5612
30.5599
0.9250
0.0748
3,1053
3,3209
60.74
OOM
Pua-Manabe
0.9729
0:00122
0.8975
47.6453
0U0101
0.5236
31.1551
019299
0.0702
2,8942
0.1827
87.74
OOM
TA. Diatuge
42.1255
0.97552
0.0011
0.9023
47,7716
0,9900
0.0100
0.5378
31.3369
0.9302
0.0727
2,6389
2,6000
998.58
OOM
Pos-LUT (Ours)
60.3555
00.9633
1,0339
43.7466
0.9922
[0.0169|
29.7376
(.9106
3,2934
0,6626
0.38
where JSDIUT(()) denotes the lookup and interpolation process in the LUT retrieval. More details about SDLUT and the quadrilinear interpolation can be found in Section A.3
3.4 Adaptive Output
For the feature channel pixels from the SDLUT (V (w.h)) V2 ((.,))) V V (w. (w,h)) V (1.,))) AOLUT adaptively aggregates channel information to generate high-resolution multispectral channel pixels ())
(dag GO Bo NTRR (w,A))- Pixel-level Transformation can be formulated as:
(w,b)) (w,h):
(Reu, (ww,yy: B.
B(., NTRR
fAOLUT (ww,)) Ky,, V V (w, V K),,)) (6) (w,h))
where fAOLUT()) denotes the lookup and interpolation process in the LUT retrieval. More details about AOLUT and the pentalinear interpolation can be found in Section A.4
3.5 Loss Function
To achieve satisfying pan-sharpening results, we propose joint loss for network training. Suppose the batch size is T. We first utilize the MSE loss:
T Σ T
1
Lmse
WHRMS, GT:
t=
where HRMS and GT denote the network output and the corresponding ground truth, respectively.
To enhance the stability and robustness of the learned LUTs, we incorporate smoothness regularization C. and monotonicity regularization Ly::
C. C. + L.D + CAO Lm = L.G LSP
AO
(8)
where CPG LSD and 0A denote the smoothness regularizations for PGLUT, SDLUT, and AOLUT, while cho' _.D'' and ÑAo represent the monotonicity regularizations for PGLUT, SDLUT, and AOLUT, respectively.
Taking SDLUT as an example, the smoothness regularization can be defined as:
C
Σ Σ
(10((11).m))) O (s...k.m) !|
OEll,o,c,a k..km==u
+ ([(0 - (b..k.mm)) 11
(9)
+ ((..s.t1,m - (z.
+ +1(O(isshmmm))
(m.)

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PAN
Brovey
HIS
PNN
MSDUNN
FlwFlow
PSCINN
Pan-Mamba
TA-DiMHOP
Ours
GT
Brivey
IRS
PNN
MISICCNN
PanFllow
PSCINN
Pan-Mambe
TA-DITTHP
Ours
Figure 3: Visual comparison on WorldView-III dataset. The row visualizes the MSE residues
between the pan-sharpening results and the ground truth.
where N represents the number of bins in each dimension the LUT. ki.jkk.m) is the corresponding output for the defined sampling point (i, k, m) LUT. The definitions of Ls and Ls are similar to those in Equation
The monotonicity regularization in AOLUT can be defined as:
N
CSD m
Σ
(g(O(i..)).)) - (
OE (1,0,c,) kj..km=0
(1.5,.m)))
((.s+IAmm))
(10)
+ ((.5...))))))
0(...A+.m))
+ t0 O(....,m+
where 9()) denotes the ReLU activation function. Similarly, LPG and LAO are defined in the same
way in Equation 10
The final loss functions are as follows:
L= L1 + Xoh., + Am
(11)
where the two constant parameters A and Am are used control the effects of the smoothness and monotonicity regularization terms, respectively. In our experiments, we empirically set X = 0.0001 and Am 10. More details about the loss functions can be found Section A.5
4 Experiments
4.1 Datasets
Remote sensing datasets from three satellites are used in experiments, including WorldView- (WV2), GaoFen2 (GF2) and WorldView-III (WV3). Due to the absence of high-resolution multispectral ground truth images in these datasets, generate the training using the Wald protocol tool (37]. Specifically, given the original MS image and its corresponding high-resolution PAN image, they are downsampled by factor of obtain image pairs of MS and PAN, with r to 4. During training, the original high-resolution MS image is treated as the ground truth, while the MS and PAN images serve as the input image pairs.
4.2 Implementation Details
We compare the proposed Pan-LUT model against several pan-sharpening methods on reduced- resolution scenes from WV2, WV3, and GF2 datasets. Specifically, we choose four traditional
7

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Table 2: Evaluation the real-world full-resolution scenes from WorldView-Il dataset. The best values are highlighted by red. The up or down arrow indicates higher or lower metric corresponding to better results.
Metrics
Brovey
1S
PNN
MSDCNN GPPNN
SEDI
UCGAN
PanFlow
PSCINN
Pan-Mamba TA-DifTIQP
Ours
DA
0.1022
0.1110 0.1057
0.1063
0.0987
0.1034
0.1042
0.0966
0.0967
0.0966
0.0953
0.0571
Ds
0,1409
0.1556 0.1446
0.1443
0,1312
0.1305
0.1476
0.1274
0.1271
0,1272
0.1129
0.0640
QNRT
0.7728
0.7527
0,7684
0.7683
0.7859
0.7827
0.7650
0.7910
0,7904
0.7911
0.8025
0.8829
PAN
MS
Brovey
1H55
PYN
MSDUCNN
GPYNN
SFDI
UCGAN
PanFlow
PSCINN
Pan-Mausha
TA-DITHOP
Ours
Figure 4: Visual comparison on the real full-resolution scenes from the WorldView-II dataset. For a more detailed examination of the results, zoomed-in view specific parts of the images.
pan-sharpening techniques: Brovey [1] IHS 22. SFIM 30 and GS (0), along with ten deep
learning-based approaches: PNN 33 PanNet [4), MSDCNN [50), Pan-GAN [32], SFDI [60]. UCGAN [58] PanFlow [48. PSCINN
Pan- Mamba 12 and TA-DiffHQP 4 Several widely
used image quality assessment metrics are employed to evaluate the performance of the algorithm, including peak segnaltto-oiis ratio (PSNR) [16], structural similarity index (SSIM) [44], spectral angle mapper (SAM) [51], relative dimensionless global error in synthesis (ERGAS) [36], spectral distortion mndex(Dx). spatial distortion index (Ds) and the quality with no reference (QNR) MI The Py PyTorch framework is implemented in experiment. During the training phase, we employ an ADAM optimizer with 31 = 0.9 and B2 = 0.999, to update the network parameters for 1000 epochs with batch size of 1. The learning rate is initialized with 5 10-4, In parallel, StepLR learning rate adjustment strategy is employed to reduce the learning rate by half after every 200 iterations. The sizes of PGLUT, SDLUT and AOLUT set to 9, 9 and 9, respectively.
4.3 Comparison with Other Methods
Evaluation on Reduced-resolution Scene. The quantitative results across three datasets are presented in Table L with the best results highlighted in red. Compared to traditional methods, Pan-LUT achieves average PSNR improvement of 5dB, 7dB. and 7dB across the three datasets, while maintaining inference speeds comparable to those of traditional methods. It is worth noting that the proposed Pun-LUT does not incorporate any network structure, yet outperforms some DNN-based methods, such as PanNet and PNN, in terms of both performance and inference time. We also provide visual comparisons for the WV3 datasets, shown in Figure
Evaluation on Full-resolution Scene. To assess the performance and generalization capability of our method on full-resolution scenes under real-world conditions, we first trained Pan-LUT on the WorldView-Il data and then tested it on unseen full-resolution WorldView-II satellite datasets. The real-world dataset consists of 200 newly collected samples from the WorldView- satellite for evaluation. The results are presented in Table 2 On reduced-resolution scenes, our method falls short of most DNN-based approaches in terms of performance. However, resolution scenes, it outperforms all of them when considering the metrics of Dx, Ds. and QNR. This demonstrates its strong generalization ability in real-world situations. Additionally, we provide a visual comparison against both traditional and DNN- based methods, as shown Figure 4
full-
Computation Efficiency Comparison. We conduct three experiments to comprehensively evaluate the computational efficiency of all methods: (1) testing the maximum image size that each method can handle 11GB and 24GB GPUs; (2) measuring their inference time on the CPU; and (3) evaluating their inference time on an RTX 2080 Ti GPU with 2K x2K and 4K x4K images. For each method, we record the average inference time on 100 images. As shown in Figure even

---

Table 3: Memory requirements (K).
Nart, N.-R, PGLUT N. M.DLUTT
Module
Ny-R, Nprs, AOLAVY AOLAT
AOLUT
0.97
31.10
236.20
5679.43
156541.57
29.76
SDLUT
0.08
1.30
6.56
83.52
1185.92
PGLUT
1.21
38.88
295.24
7099.28
195676.96
PSNR
29.74
29.72
Table 4: Ablation study of PGLUT, SDLUT and AOLUT on the WorldView-Ill dataset.
29.70
29.68
Config
PSNKT
SSIM
SAMI
ERGASL
@ only PGLUT
25.2788
0.7742
0.1130
5.4852
(i) only SDLUT
26.1433
0.8165
0.1123
5.4211
size (N)
only
28,9554
0.8677
0.1001
Different sizes of the LUT.
(iv) PGLUT
29.1315
0,8795
Figure 5: Ablation studies on different sizes of the PGLUT, SDLUT and AOLUT the WorldView-III dataset.
(v) SDLUT and AOLUT
29.2754
0.9010
0.0934
3.6033
(vi) PGLUT and AOL
29.4875
0.8925
0.0900
3.4977
Pan-LUT (Ours)
29.7376
0.9106
0.0815
with 24GB GPU, existing methods fail process x&88 images, while method handle 9K*9K images on an 1 11GB GPU. In environments without GPU acceleration, most methods exhibit unsatisfactory inference speed. As shown Table D Pan-LUT efficiently processes images at all resolutions. Compared to DNN-based methods, it achieves significantly faster inference speeds, while maintaining comparable speed to traditional methods. Our method easily meets the real- time processing requirements on GPUs, outperforming all other methods by substantial margin. Notably, only PNN is capable of handling remote sensing satellite images at the 4K 4Kx4K resolution, highlighting the superior efficiency of our approach.
4.4 Ablation Study
Size of Look-Up Tables. As shown in Figure changing the LUT size does not lead to significant drop in performance. This observation suggests that the effectiveness of our proposed method not dependent on consuming extensive storage resources to increase the LUT size. First, examine the effect of PGLUT size, denoted Np. Performance improves with larger Np, reaching an optimal point values Np 9. Beyond this (from to 17), only minor gain of 0.01 dB is observed, while the number of parameters increases substantially from 236K to 5M, indicating capacity redundancy. Therefore, we set =P as the default balance performance with storage requirements. For SDLUT, denoted Ns, increasing Ns from 3 to 9 improves performance, but values above cause slight performance drop. Similarly, enlarging the AOLUT size NA yields only minor gains but substantially increases parameters, especially beyond NA = 9. We thus set N, = 9 to balance performance with computational efficiency. We provide the parameter count for each LUT of different sizes in Table 3, which can be calculated as follows:
ParamPGLUT 5 ParamspUUT N Param 4
(12)
Effectiveness of Each LUT. We further conduct ablation studies verify the effectiveness of each LUT. Results are listed in Table Our observations are as follows: 1) Comparing (i) with (iv) and (ii) with (v), SDLUT effectively captures fine-grained spatial details from the PAN image, thereby enhancing overall performance. 2) Comparing (ii) with (iv) and (iii) with (iv), PGLUT provides finer control over spectral transformation, resulting improved performance. 3) Comparing (ii) with (v) and (i) with (vi), AOLUT demonstrates adaptive aggregation capabilities.
5 Conclusion
In this paper, we propose a novel learnable LUT framework, called Pan-LUT, which strikes an optimal balance between performance and computational efficiency for high-resolution remote sensing images pan-sharpening. The proposed method makes possible to process 15K x 15K remote sensing images on a 24GB GPU and processes 9K x9K image in under ms using one RTX 2080 Ti GPU. Extensive experiments on various satellite datasets demonstrate the effectiveness and efficiency of Pan-LUT.

---

6 Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 82172073 and Grant 02271430, and in part by the Open Fund of the National Key Laboratory of Infrared Detection Technologies.
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13

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A Appendix
The following contents are provided in the appendix:
Limitation and Broader Impact.
More Technical Details.
More ablation study.
More comparison experiments.
More visualization about our experiments.
A.1 Limitation and Broader Impact
Limitation. One limitation of existing methods is the lack of neural network integration. While combining LUTs with neural structures may offer greater modeling capacity, it often comes the cost of computational efficiency. Conversely, LUTs alone require substantial storage space, making them less suitable for high- dimensional input processing. The space complexity of an n-dimensional LUT is O(N D*). meaning its size increases exponentially with higher dimensions. This exponential growth severely limits its practical applicability, particularly when dealing with high-dimensional inputs. Lightweight network architectures must be further explored facilitate efficient integration with LUTss. Mcanwhile, efficient LUT designs are essential for processing nigh-dimensional inputs.
A.2 Details of PGLUT
Input: Suppose that the MS image (MS E R#/xxW/exO) and the PAN image (P RHxWx1) are given, the input of PGLUT (lpG E H XW (C++) can be formulated as:
IPG Concat(P, MS 1t,
(13)
where Concat(-) is the concatenation operation. MS 1 denotes the upsampled MS image. MS and P have the same spatial resolution.
Output: the output of PGLUT (OpG E (elIWx((C+)) can be formulated as:
OPG = FPGLUT(IPG):
(14)
where FrGLUT()) denotes the lookup and pentalinear interpolation based on the PGLUT.
PAN-guided indexing strategy. MS image, pixels from different spatial locations may have identical values (e.g., ri Ti
bj = by. nur; = nirj, where i & j). The LUT maps these identical inputs to the same output. The PAN-guided indexing strategy provides more flexible indexing mechanism for the LUT. Specifically, it additionally considers the pixels corresponding spatial positions in the PAN image (e.g.. r; = 94 44 by, vér; nérz, pa, F paj. where * j). thereby achieving fineregraieed mapping. Given an input value T(w.A) {pa{ (w. Ro b/ bj. nér
(w. rér((...)) }. where (u, f)) denotes the spatial position of pixel in the image. the input index the PGLUT based on the input value be represented (x.
y. z. s, e). PGLUT index in the PGLUT. We use (i,j. k, m. n1) to denote the coordinates of a defined sampling point in PGLUT, which can be calculated as follows:
performs a lookup operation to locate nearest 32 adjacent elements around the input
po( Vmaa
(((o,h)
Kem,hi Vmaa
I
-N N
N,
N,
Vinax
N.e nir((w,A) N. Vmaz
(15)
Vmaz
1= [x],] = Lv], k [2], m [s], Le].
where Vmaa denotes maximum value (e.g., 255. 1023 or 2047). [.] denotes floor function. Then, offset hetween the input precise index (I, y. 2, e) and the computed sampling point (i, k, m, n) can be computed:
d. i,d, y j. d.
k.
d, m, de e m, d_ = dz, d dy, d= = ds.
(16)
d_,, = 1 d,, d_a = 1 de.
14

---

Pentalinear Interpolation. After locating 32 adjacent points, an appropriate interpolation technique is applied
generate the output value using the values of these sampled points:
OpGla.......)) d- yd ( + d.d d4
-d= (m) + + d (t.j. 1,m,))
(...k.mt
(1.. k.m.m+1
,d
(.t...Atm...
+d=d_ap_ (+1jj.tim
d.d dpd
+d_zdyd,d_ad (1.))
dd -___dd_dd-0
(1. k1,,
+d_ad,d_
_ d d
+d
+ d_ , d (.....
(17)
+dgd,d,d
d, -_4,,d_d..0
+d -d (1115-+ +ddd,dddd,d + +d d. (1+)_
dzd-,d 1,m
+ 0d..:...ddddddd000,,,,,...- ddd.d,dpdd_..
(ddd,d
+ddd_dd,d,
+d.,d,d,ddde (11.00001111111111.1.1. d. (1-1))0000011111111.1
where O (....k.m..)) represents the value of the LUT the coordinate (i, m).
A.3
Details of SDLUT
Input: The input image (Opg E ((+W)((+ of SDLUT is the output of PGLUT.
Output: The output of SDLUT (OsD R [(X be formulated as:
OSD = FSDLUT(OPG),
(18)
where FSDLUT(-) denotes the lookup and quadrilinear interpolation based the SDLUT.
Look Up. Specifically, for each channel of input, SDLUT operates by iterating through pixels
time, treating each the current pixel during processing. Given the input current pixel and adjacent pixels, which denoted as P(w.h)> P(w++11h)> P(w.k=1) and P(.++1.A.+1 respectively, the input index to the SDLUT based on the input can be represented as (Ir, 2, 8). SDLUT first performs a lookup operation to locate the nearest 16 adjacent elements around the input index in the SDLUT. We use (i.j. k. m) to denote the coordinates of defined sampling point in PGLUT, which can be calculated follows:
P(w.A)
N. P(w+1.h) N,
Vman
Vmaz
P(w,k-1)
N, P(w+l,6+1) -N,
(19)
Vmaz
Vmaz
[m],] = [y], ks = [a], [s],
where Vinar denotes the maximum pixel value. [.] signifies the floor function. Then, the offset between the input precise index (Ir, Mi 2, 8) and the computed sampling point (i. ki m.) can be computed:
d_ de, d_ dy: di i, dy y/ - d,
d, m,
(20)
d_ d,y d_, d..
Quadrilincar Interpolation. After locating 16 adjacent points, an appropriate interpolation technique is applied generate the output value using the values of these sampled points:
O OsD(z.u.S.e) = d d
(t..k...)) +dd-.dd=dd (
+d dd 0 (s.
+ (+1.).tm)1))
(21)
td (1. + (1....
+d-.d_, (1 0 +
+dpd,d (44.:d__g..00414.)1...... d (4d
+d (.g.dddddd.tttttmmmm..m +
15

---

where (t....km)) represents the value of the LUT at the coordinate (i.j, m.).
Rotation-indexing strategy. In general. the performance of the SDLUT can be improved when more pixels are considered. In order exploit more in the image, we Rotation-indexing strategy in the training phase. For our SDLUT, 4 rotational ensemble with O, 90, 180, and 270 degrees covers total x pixels. Each output from the 4 rotations is defined as follow:
(i FSDLUT (P(w,)) P(i++ (16)+1,66:00
P(w,,4+ P(w, P(.++,1441 1,1+1))
2 P( = PSDLUT (P(w.)) P(w+1.1) Pii+tt.)-PPwttthh. P(w+1h-1) Ptw,))
(22)
3 Pimm, = ESDLUT (P(,,)) (,0+1,6) Pi, P(w+
Os OsD(w.)) = PSDLUT (Pi P(w+ 1,4) P(w,++))+ P(w+1
where FSDLUT(-) denotes the lookup and quadrilinear interpolation based on the SDLUT.
Proof Let (w0, h) denote the pixel located at the w-th column and h-th row of the image. We provide a theoretical
proof that Rotation-indexing strategy (RiS) extends the receptive field from 2 x 2 to 3 x 3. Idcally, we aim to incorporate all pixels within the local 3 3 region centered at (we h), corresponding the input index set:
Gigt = {(w - 1, 1), (w; h 1), (w + 1, 1), (w 1, (w, h), (w + 1, h), (w 1, + 1), (w, h + 1), (w + 1, + 1)}-
(23)
By default, SDLUT captures the bottom-right neighborhood each pixel, with corresponding input index group defined as:
Goo {ww, h), (w + 1, h), (w., + 1), (w + 1, 1)).
(24)
With the proposed RiS SDLUT is applied to three rotated versions of the input, thereby effectively capturing broader set of pixel neighborhoods.
Goo0 = {(w, ht), (w - 1,)), (w, h+ 1), (w 1,/ 1)}, G11s0 = {(w, h), (w 1, h), (w. h (w, - 1, 1)}
(25)
Gr270 {(w,h) (w + 1, 1), (w,, h 1), (w 1, 1)}.
Then, we can derive the following equation:
G.o U Gr90 Griso U Gr27o Gegt-
(26)
A.4 Details of AOLUT
Input: The input of AOLUT the output from the SDLUT (OsD E R(XWW((++)
Output: the output of AOLUT AO E #R XWxxXxx can formulated as:
O FooDoTTOsDD)
(27)
where FAOLUT()) (-) denotes the lookup and pentalinear interpolation hased on the AOLUT.
Given an input value [(m..)) (( ((.((..): 5a
sD( OSD(u (DD(w.n) where (w, h) denotes the spatial position of a pixel the image, {ei, c2, c3, ca, c4, ) denote the different channels of OSD. The input index to the PGLUT based on the input value can be represented as (X, y. Z, s, e). PGLUT first performs a lookup operation to locate the nearest 32 adjacent elements around the input index in the PGLUT. We use (i, k, m. denote the coordinates of a defined sampling point in PGLUT, which can be calculated as follows:
OeD(wn.) Vmaa
O OSD(wwh),
O (D(w.))
N.
N,
N,
Vmaz
Vmaz
O STD(WAA))
0 (D(()A)) N,
(28)
N,
Vmax
Vinzz
[x]
Ly], k = [=], [s|, [e].
where Vmax denotes the maximum value (c.g., 255, 1023 or 2047). |.] denotes the floor function. Then, the offset between the input precise index (x, y, 2, e) and the computed sampling point (1, k, m. n) can be computed:
d.f i,d, y j. d.
k.
d, m, de e m, d_ = dz, d dy, d= = ds.
(29)
d_, = 1 d,, d_e = 1 - de.
16

---

Pentalinear Interpolation. After locating 32 adjacent points, an appropriate interpolation technique is applied
generate the output value using the values of these sampled points:
O 2. -d
d (1541, + +d= (1. (1..ktt...m
(L..k.mm d_ -_= (1.. 4d- (utkaaoddddddd..kkkkkk....a
+ (
0 (.1...ttmmmmm
O (1t1.,tmm11 + -d de 0 k.,.,,mm p
+ ( (1,+1,
d=
+d= (...+.........
d
+1.m+1)))
+ ( +
+d-.d_, (1.
m
(30)
+d,d,d__d,dd
+
+dzd_yd.d,d
+ +
dddd,yd
+
+d_.d,d.
+ +d.d,d_d.d_.. +d.d dd
+d=2d,d= d. (1.t
dgd.dgdd
d2d-yd
td-zd,d,d;dd wtw.00000................ dxdgdadede (tt.
where (m.j.)....)) represents the value of the LUT at the coordinate (f. J. m.
A.5 Details of Loss Function
To achieve satisfying pan-shane san-sharpening results, we propose a joint loss for network training. Suppose the batch size is T. We first utilize the L1 loss:
T Emor T Σ |HRMS, -ITIMS.-GT||...
(31)
where H RMS and GT denote the network output and the corresponding ground truth, respectively.
To enhance the stability and robustness of the learned LUTs, we incorporate smoothness regularization C, and monotonicity regularization L.::
L. C
PG + L.
L AO
(32)
f Lm CSP CAO
(33)
where LPG L.P. and CAO0 denote the smoothness regularizations for PGLUT, SDLUT, and AOLUT, while CPG L.D and CAO represent the monotonicity regularizations for PGLUT, SDLUT, and AOLUT, respectively.
The Smoothness Regularization of PGLUT and AOLUT:
N
PG CAO
Σ
(10
OE(Lonaee) n.R.K.m
+ -1100rs4tmmmmm |
((..kkmmmm)
(34)
+ ||0(G..eet O ((ns.kmm.u)
The Monotonicity Regularization PGLUT AOLUT:
N-1
CPG L.
Σ
(s(O
OE(l.o.c.o.)) k -(++..A.mm)) ((O(k..k.m.))) 0(..-1kkm.)) +g +g((O(...k.m.)) 0....++,mm.))
(35)
+g(O - O(...)mmt).))) +g((Ois.A..m))) O(..s.......t))).
17

---

Table 5: Ablation study Am
Table 6: Ablation study X
Metries
0
0.1
10
100
Metrics
0,00011
0,001
0.01
0.11
PSNR
29.5321
29,5421
29 29,6211
29.7376
29.4122
PSNR
29.4746
29.4889
29.7376
29 5423
29.5215
where represents number of bins each dimension the LUT. k...kkmm.) is corresponding output for the defined sampling point (f, & m, n) in LUT. g()) denotes the ReLU activation function.
The Smoothness Regularization SDLUT:
C
Σ
Σ
OE(L.c.,a) fd..m=DD
+1(0(s--1Amm)
O Ossiitmmol
(36)
+ +1[0(eAAt1.) Oiss.mmil
+1(0mstttt (esikmm |1:)
The Monotonicity Regularization SDLUT:
Lm
Σ
Σ
(a(O(u..)..))
Oc(laie,) k...
O((t)..)m))) + ((O O(s,+1,km))
(37)
+g(O( - ((1..41.m)
+g(O (...m.m11)]
where N represents the number of bins in each dimension of the LUT. ((....,m) the corresponding output for the defined sampling point (i, J, k. m) LUT. g(-) denotes the ReLU activation function.
The final loss function is as follows:
C Aah. Amfm:
(38)
where the two constant parameters A and Am are used to control the effects of the smoothness and monotonicity regularization terms, respectively, In our experiments, empirically set A. 0.0001 and A. = 10.
A.6 Selection of regularization parameters.
As shown in T we vary the ds and Am to determine the optimal parameters (Am 10 and A = 0.0001). large X (e.g., 0.0001) results in worse PSNR, as the smooth regularization limits the flexibility of LUT transformations. In contrast, the PSNR is insensitive to the choice of Am since monotonicity is natural constraint L.TTs.
A.7 More Comparison With Traditional methods
We further compare our method with additional traditional approaches, shown in Table While significantly surpassing traditional methods in performance across the three datasets, approach maintains comparable processing speed, highlighting its practical efficiency. Notably, traditional methods only time- consumiin but also inefficient.
A.8 More Visualization about Experiments
As shown in Figure Fnd Figure 7 extensive visual comparisons are provided for the WV2 and GF2 datasets. More visual comparisons are provided supplementary material.
18

---

Table 7: Quantitative comparison across three satellite datasets with traditional methods. The best outcomes are highlighted in red. t iidicaatss better performance with increasing values, while signifies improved performance with decreasing values.
Method
WorldVien-1
GaoFen2
Worldview-111
(ms)
PSNRT
SSIMT
(SAM_
BRGAS]
PSNR
SSIMT
SAM,
BRGAS!
PSNR
SSIM
SAM]
ERGAS,
2X
2K
4K x-4K
PCA
20.3542
0,70002
0.3744
10.8524
19.2933
0.7974
0.3908
14.322
20.4455
0.5263
0.2699
10.3129
0.21
0.23
Brovey
0.0218
IHS
35.2962
0.9027
0.0161
2,0278
38.1754
0,9100
0.0243
1.5336
22.5579
0.5351
0.1266
8.3616
0.23
0.26
SPIM
34.1297
0.8975
0.0449
2.3449
36.0060
0.8822
0.0318
1.7998
21.8212
0.5457
0.1208
8.9730
0.32
0.47
Wavelet
GS
35.6376
0.91776
010422
1,8774
37.2260
0.9034
0.0309
1.6336
22,5608
(.5477
0.1211
8.2433
0.75
0.87
GSA
35.35744
0.9219
0.097
1.7401
35.948
0.8779
0.0368
1.9257
21.8945
0.5458
0.1394
9.0781
0.73
0.76
PRACE
34.9671
019063
0.04144
1,8725
36.2015
0,8902
0.0372
1.8312
22,4452
0,5535
0.1375
8.2961
2.44
4,75
AWLP
32 2402
0.8709
0.0457
2.4077
37.2183
0.8917
0.0281
1,5966
21.5792
0.5323
0.1260
9.0636
8.39
14.17
31,3946
0.0314
1.5604
Pun-LUT (Ours)
10.8555
0.9633
0.0251
1,0339
13,7166
0.9726
0.0169
0.8027
29.7376
0.9106
0.0815
3,3934
0.38
0.51
PAN
MS
Brovey
IHS
PNN
MSDCNN
PanFlow
PSCINN
Pan-Mamba
TA-DinHOP
Ours
Brovey
TA-DIIHH
Figure 6: Visual comparison on WV2 dataset. The last row visualizes the MSE residues between the pan-sharpening results and the ground truth.
PAN
MS
Brovey
IHS
PNN
MSDCNN
PanFlow
PSCINN
Pan-Mamba
TA TA-DiITHP
Ours
GT
Brovey
IHS
PNN
MSDD NN
PanFlow
PSCINN
Pan-Marhha
TA-DITTHOP
Ours
Figure Visual comparison on GaoFen2 dataset. The last row visualizes the MSE residues between the pan-sharpening results and the ground truth.
19

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Reviews:
[{"review_id": "ghUMzJgrvS", "reviewer": "Reviewer_Znrg", "summary": "In this paper, to achieve high computational efficiency and a lightweight design, the authors propose a fully LUT-based framework. Specifically, they design three types of LUT modules: PGLUT for spectral information exchange across channels, SDLUT for capturing fine-grained local spatial details, and AOLUT for aggregating multimodal information to produce adaptive high-resolution results. Experimental results demonstrate that the proposed method achieves superior computational efficiency while delivering performance comparable to state-of-the-art deep learning-based approaches.", "questions": "1. Pan-LUT-N denotes the model variant with N stacked PGLUT and SDLUT modules, analogous to increasing network depth. It would be valuable to evaluate its generalization ability across different resolutions by testing on full-resolution images and assessing the potential risk of overfitting.\n\n2. The rotation ensemble strategy is typically used as a Test-Time Augmentation (TTA) technique. However, its effectiveness during training remains unclear and should be validated through ablation studies or comparisons with appropriate baselines.\n\n3. Can quadrilinear and pentalinear interpolation be regarded as higher-dimensional extensions of bilinear interpolation?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. This method is capable of processing high-resolution images even in resource-constrained environments. As demonstrated in the paper, it can handle images with resolutions up to 8K and 16K.\n\n2. The proposed method is novel, fast, and computationally efficient, with a simple yet effective design that makes it practical for real-world applications.\n\nWeaknesses:\n\n1. As mentioned in the paper, the storage requirements increase substantially with the size and number of LUTs, which is an inherent limitation of LUT. This reflects a trade-off between performance and computational efficiency, as well as between memory requirements and processing speed.\n\n2. Some details in the writing are not clearly presented. The paper uses two different forms of resolution description. In Section 1, the terms \"2K\" and \"4K\" are used, while in Section 4.3 (line 269), the resolutions are specified as 2048 × 2048 and 4096 × 4096. It is unclear whether \"2K\" specifically refers to 2048 × 2048. This inconsistency may cause confusion and should be clarified.", "was_revised": true, "final_justification": "The author added additional experiments to address the issues I was concerned about and provided detailed formula derivations, so I decided to increase my score.", "rebuttal": "Thank you for your review.\n\n# Weakness 1\n\nThere is a trade-off between performance and computational efficiency associated with LUT-based methods. However, this overhead is acceptable given the performance benefits achieved, especially in real-time inference and high-resolution image processing scenarios.\n\n# Weakness 2\n\n\"2K\" specifically refers to a resolution of $2048\\\\times2048$, while \"4K\" refers to $4096\\\\times4096$, we will revise the manuscript to clarify the actual image sizes used in our experiments to avoid ambiguity.\n\n# Question 1\n\nWe conduct additional experiments to evaluate more Pan-LUT-N variants, where $N$ denotes the number of stacked PGLUT and SDLUT modules. \n\n| Method | WV2 | GF2 | WV3 | Param(M) | Inference(ms) | Inference(ms) |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n||||||$2048\\\\times2048$|$4096\\\\times4096$\n| Pan-LUT-1 | 40.6362 | 43.6559 | 29.6213 | 0.9579 | 0.3812 | 0.5445 |\n| Pan-LUT-2 | 40.8419 | 44.5545 | 29.7929 | 1.3843 | 0.9434 | 1.0824 |\n| Pan-LUT-3 | 41.1362 | 44.8559 | 30.1213 | 1.8108 | 1.4821 | 1.9648 |\n| Pan-LUT-4 | 41.1624 | 44.9756 | 30.1913 | 2.2373 | 2.0621 | 2.7518 |\n| Pan-LUT-5 | 41.2854 | 45.1733 | 30.2471 | 2.6637 | 2.3712 | 2.9915 |\n| Pan-LUT-6 | 41.4025 | 45.3364 | 30.3087 | 3.0902 | 2.7915 | 3.1024 |\n\nAs shown, deeper models (e.g., Pan-LUT-4 to Pan-LUT-6) offer only marginal improvements in performance compared to shallower variants (Pan-LUT-1 to Pan-LUT-3), while significantly increasing parameter count. For example, Pan-LUT-4 already exceeds 2 million parameters, which limits its practicality. Therefore, we adopt Pan-LUT-1, 2, and 3 as the default architectural choices.\n\n| Metrics | Pan-LUT-1 | Pan-LUT-2 | Pan-LUT-3 | Pan-LUT-4 | Pan-LUT-5 | Pan-LUT-6 |\n| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |\n| $D_{\\\\lambda} \\\\downarrow$ | 0.0956 | 0.0962 | 0.0978 | 0.0963 | 0.0987 | 0.0944 |\n| $D_{S} \\\\downarrow$ | 0.1145 | 0.1253 | 0.1276 | 0.1269 | 0.1212 | 0.1233 |\n| QNR$\\\\uparrow$ | 0.8024 | 0.7985 | 0.7948 | 0.7934 | 0.7959 | 0.7986 |\n**Dataset: Full-WV-II**\n\nAlthough deeper architectures often risk overfitting, the consistent performance of Pan-LUT models on unseen datasets indicates that our method maintains strong generalization without overfitting the training data.\n\n# Question 2\n\nWe demonstrate the advantages of the Rotation Ensemble Strategy (RES) from both theoretical and empirical perspectives.\n\n**Reducing Memory Usage.**\nThe RES effectively expands the receptive field of the SDLUT module from $2 \\\\times 2$ to $3 \\\\times 3$. Without RES, achieving the same receptive field would require increasing the LUT dimensionality from 4 to 9, leading to a memory complexity of $O(N^9)$. In contrast, RES enables a larger receptive field without increasing LUT size, thereby reducing memory usage.\n\n**Local Texture Enhancement.**\nLet $(w, h)$ denote the pixel located at the w-th column and h-th row of the image. We provide a theoretical proof that RES extends the receptive field from $2 \\\\times 2$ to $3 \\\\times 3$.\nIdeally, we aim to incorporate all pixels within the local $3 \\\\times 3$ region centered at $(w, h)$, corresponding to the input index set:\n\n$G_{tgt} = \\\\{(w{-}1,h{-}1), (w,h{-}1), (w{+}1,h{-}1), (w{-}1,h), (w,h), (w{+}1,h), (w{-}1,h{+}1), (w,h{+}1), (w{+}1,h{+}1)\\\\}.$\n\nBy default, SDLUT captures the bottom-right neighborhood of each pixel, with the corresponding input index group defined as:\n\n$G_{r0} = \\\\{(w,h), (w+1,h), (w,h+1), (w+1,h+1)\\\\}.$\n\nWith the proposed **RES**, SDLUT is applied to three rotated versions of the input, thereby effectively capturing a broader set of pixel neighborhoods.\n\n$G_{r90} = \\\\{(w,h), (w{-}1,h), (w,h{+}1), (w{-}1,h{+}1)\\\\}$,\n\n$G_{r180} = \\\\{(w,h), (w{-}1,h), (w,h{-}1), (w{-}1,h{-}1)\\\\}$,\n\n$G_{r270} = \\\\{(w,h), (w{+}1,h), (w,h{-}1), (w{+}1,h{-}1)\\\\}$.\n\nThen, we can derive the following equation:\n\n$G_{r0} \\\\cup G_{r90} \\\\cup G_{r180} \\\\cup G_{r270} = G_{tgt}.$\n\n\n| Method | WV2 | GF2 | WV3 |\n| :---: | :---: | :---: | :---: |\n| Pan-LUT-1 | 40.6362 | 43.6559 | 29.6213 |\n| Pan-LUT-1 (w/o RES)| 40.4993 | 43.2147 | 29.3354 |\n| Pan-LUT-2| 40.8419 | 44.5545 | 29.7929 |\n| Pan-LUT-2 (w/o RES)| 40.7247 | 44.3754 | 29.6786 |\n| Pan-LUT-3| 41.1362 | 44.8559 | 30.1213 |\n| Pan-LUT-3 (w/o RES)| 40.9988 | 44.6672 | 30.0455 |\n\n**Definition:** w/o RES' means the framework without the Rotation Ensemble Strategy.\n\nWe find that RES not only contributes to performance improvement but also significantly reduces the number of parameters.\n\n# Question 3\nThe proposed quadrilinear and pentalinear interpolation methods are conceptually similar to linear and bilinear interpolation, serving as their extensions to higher dimensions. Below, we provide a theoretical analysis to illustrate their similarities.\n\n**Linear Interpolation.**\nGiven two known data points $P_0=(x_0,y_0)$ and $P_1=(x_1,y_1)$, we assume the function changes linearly between these two points. We then estimate the corresponding $y$ value for a target point $x$ based on its proportional position between $x_0$ and $x_1$.\nSpecifically, if a point $x$ is between $x_0$ and $x_1$, and its distance to $x_0$ is $t$ times the total distance $|x_1−x_0|$ (where $0≤t≤1$), then $x=x_0+t(x_1−x_0)$.\nSimilarly, its corresponding $y$ value, $y$, can be expressed as $y=(1-t)y_0+ty_1$.\n\n**Bilinear Interpolation.**\nSuppose we are given function values $f$ at four corner points on a 2D plane: $Q_{11}=(x_1,y_1)$, $Q_{12}=(x_1,y_2)$, $Q_{21}=(x_2,y_1)$, and $Q_{22}=(x_2,y_2)$. We aim to estimate the function value $f(x,y)$ for any point $P=(x,y)$ on the plane.\n\n**Step 1: Linear interpolation along the x-direction.**\n\nFirst, along the row where $y_1$ is constant, linearly interpolate the point $(x, y_1)$. Using the values of $Q_{11}=(x_1, y_1)$ and $Q_{21}=(x_2, y_1)$, calculate the value for $R_1=(x, y_1)$:\n\n$f(R_1) = \\\\frac{x_2 - x}{x_2 - x_1} f(Q_{11}) + \\\\frac{x - x_1}{x_2 - x_1} f(Q_{21})=d_xf(Q_{11})+(1-d_x)f(Q_{21})$,\n\nwhere $d_x = \\\\frac{x_2 - x}{x_2 - x_1}$ denotes the relative distance to the known points.\n\nThen, along the row where $y_2$ is constant, linearly interpolate the point $(x, y_2)$. Using the values of $Q_{12}=(x_1, y_2)$ and $Q_{22}=(x_2, y_2)$, calculate the value for $R_2=(x, y_2)$:\n\n$f(R_2) = \\\\frac{x_2 - x}{x_2 - x_1} f(Q_{12}) + \\\\frac{x - x_1}{x_2 - x_1} f(Q_{22})=d_xf(Q_{12})+(1-d_x)f(Q_{22}).$\n\n**Step 2: Linear interpolation along the y-direction.**\n\nFinally, using the values of $R_1=(x, y_1)$ and $R_2=(x, y_2)$, linearly interpolate for the target point $P=(x, y)$:\n\n$f(P) = \\\\frac{y_2 - y}{y_2 - y_1} f(R_1) + \\\\frac{y - y_1}{y_2 - y_1} f(R_2)=d_xd_yf(Q_{11})+(1-d_x)d_yf(Q_{21})+d_x(1-d_y)f(Q_{12})+(1-d_x)(1-d_y)f(Q_{22}),$\n\nwhere $d_y =  \\\\frac{y_2 - y}{y_2 - y_1}$ denotes the relative distance to the known points.\n\n**Quadrilinear Interpolation in Look up table.**\nAll the interpolation methods above exhibit a structurally similar form in estimating the target value:\n\n$y=(1-t)y_0+ty_1$,\n\n$f(P) = d_xd_yf(Q_{11})+(1-d_x)d_yf(Q_{21})+d_x(1-d_y)f(Q_{12})+(1-d_x)(1-d_y)f(Q_{22})$.\n\nWe show that the estimation formula of quadrilinear interpolation follows the same functional form.Since quadrilinear interpolation involves 16 known points, the full expression is too lengthy to present here. For more details, please refer to the technical details section in the supplementary material."}, {"review_id": "mqmWw54FmQ", "reviewer": "Reviewer_sEAR", "summary": "Pan-LUT bridges the gap between computational efficiency and performance in high-resolution pan-sharpening. By replacing complex DNN operations with learnable LUTs, it achieves speedup over SOTA methods while maintaining competitive accuracy. This work pioneers hardware-friendly LUT designs for remote sensing, with implications for real-time Earth observation systems.", "questions": "1. This paper aims for \"real-world applicability\" but ignores key satellite-specific resolution variations, undermining claims like bridging the gap to real-world applications. This method has been experimentally tested with a fixed downsampling factor of 4. How to deal with situations where the downsampling factor is not fixed?\n\n2. This method has only been validated in scenarios with a small number of spectra. How to deal with a large number of input data spectra?\n\n3. In this paper, it is mentioned multiple times that the application of existing DL methods is limited in scenarios without GPUs and CPUs, overlooking critical edge device validation despite claiming “lightweight” and “hardware-friendly” attributes. Can comparative data be provided on edge devices?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. This paper proposed a learnable LUT framework for pan-sharpening, breaking computational bottlenecks of traditional DNNs.\n\n2. Pan LUT consists of three core modules: PGLUT for controlling the spectral transformation, SDLUT for capturing spatial details, and AOLUT for adaptively Aggregating channel information.\n\n3. Pan LUT is capable of processing ultra-high resolution (such as 4k) images, which many methods cannot achieve.\n\nWeaknesses：\n\n1. There is still a gap compared to SOTA methods in terms of spectral fidelity and spatial detail preservation. In addition, Lightweight is one of the characteristics of the proposed method, but it has not been compared with recent lightweight methods in experiments.\n\n2. There is no specific explanation on how SDLUT processes boundary pixels in section 3.\n\n3. The dataset used in the experiment has a limited number of spectra, and the proposed method did not consider how to cope with the increase in the number of spectra, especially in hyperspectral scenarios.", "was_revised": true, "final_justification": "The authors have addressed my concerns. I lean to keep the score.", "rebuttal": "Thank you for your review.\n# Weakness 1\n[1] LGPConv-Learnable-Gaussian-Perturbation-Convolution-for-Lightweight-Pansharpening. IJCAI 2023\n\n[2] Deep Adaptive Pansharpening via Uncertainty-aware Image Fusion. TGRS 2023\n\n[3] SSUN-Net: Spatial-Spectral Prior-Aware Unfolding Network for Pan-Sharpening. AAAI 2025\n\nUAPN-S is the most lightweight model variant proposed in [2]. SSUN-Net-1stg is refers to the version with a single reconstruction stage in [3]. \n| Method | WV2 | GF2 | WV3 | Param(M) | Inference(ms) | Inference(ms) | Inference(ms)| Inference(ms)|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n|||||| $2048\\\\times2048$| $4096\\\\times4096$|$2048\\\\times2048$ (CPU) |$4096\\\\times4096$ (CPU) |\n| Pan-LUT-1 | 40.6362 | 43.6559 | 29.6213 | 0.9579 | **0.3812** | **0.5445** |**1012.15**|**5338.36**|\n| Pan-LUT-2 | 40.8419 | 44.5545 | 29.7929 | 1.3843 | **0.9434** | **1.0824** |**1945.78**|**9664.84**|\n| Pan-LUT-3 | 41.1362 | 44.8559 | 30.1213 | 1.8108 | **1.4821** | **1.9648** |**2844.32**|**12523.24**|\n| LGPConv | **41.6742** | 46.6645 | **30.4232** | **0.0262** | 27.15 | OOM |25113.29|126547.26|\n| UAPN-S | 41.5663 | **46.8212** | 30.3065 | 0.0345 | OOM | OOM |78775.02|463461.26|\n| SSUN-Net-1stg | 41.6663 | 46.6343 | 30.3371 | 0.0596 | 163.69 | OOM |34446.80|202476.98|\n\nWe show that current lightweight methods are only lightweight in the context of low-resolution images. These approaches are highly sensitive to input image size and rely heavily on specialized hardware such as GPUs or TPUs. As shown in Table, in the absence of GPU acceleration, these methods require a considerable amount of time to process images. UAPN-S is particularly sensitive to input resolution and fails to process $2048 \\\\times 2048$ images even on a single 2080Ti GPU.\n\n# Weakness 2\n\nTo handle boundary pixels, we adopt a reflect padding operation, which pads the image by mirroring adjacent pixels at the borders.\n\n# Weakness 3 and Question 2\n\nTo handle a larger number of spectral channels, one can simply increase the dimensionality of PGLUT. However, this leads to a rapid growth in the number of parameters. For example, in the case of PGLUT with 12 input spectral channels and a LUT size of 3, the number of parameters reaches $12 \\\\times 3^{12} = 6{,}377{,}292$. Since LUTs store precomputed output values to avoid redundant computation, the resulting parameter explosion is inevitable. A practical solution is to incorporate smart architectural designs—such as the proposed Rotation Ensemble Strategy (RES)—to mitigate the memory and parameter overhead. \n\n**Rank Factorization**. In multispectral and hyperspectral scenarios, rank factorization offers a promising direction to reduce the parameter count and memory consumption of the model. We plan to explore this strategy in future research.\n\n# Question 1\n\nTypically, the LRMS images are upsampled to match the PAN resolution using bicubic interpolation before being fed into model, which is a widely adopted practice in pansharpening literature. Our method follows this approach and is inherently compatible with varying resolution ratios. Regardless of the original downsampling factor, once the LRMS image is interpolated to the PAN resolution, Pan-LUT can be applied without requiring any architectural modifications. This ensures the robustness and applicability of our method in real-world scenarios with diverse resolution settings.\n\n# Question 3\n\nOur method focuses on scenarios involving high-resolution image processing, particularly in environments where GPUs or TPUs are unavailable, as stated in Lines 6 and 50 of the manuscript. As shown in the table provided in our response to Weakness 1, our method can be efficiently deployed on CPUs while maintaining computational efficiency. In practice, many edge devices, such as the NVIDIA Jetson and Rockchip RK series, are equipped with CPUs. And our method can be deployed on any edge device equipped with a CPU."}, {"review_id": "HNCxjhZhDd", "reviewer": "Reviewer_QT6g", "summary": "The paper introduces Pan-LUT, a look-up table (LUT) based method designed for the pan-sharpening task. Unlike contemporary deep learning-based approaches that require significant computational resources due to their complex network architectures, the proposed method is computationally efficient. The proposed framework incorporates three types of learnable LUTs that map values from low-resolution multispectral inputs to high-resolution multispectral outputs.", "questions": "1. The authors need to analyze the impact of different LUT sizes on inference time.\n2. What is the maximum image resolution that the proposed method can handle on the specified GPU?.\n3. What contributions does the rotation ensemble strategy outlined in Section 3.3 make to the performance or robustness of the approach?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The idea of using look-up table for efficient pan-sharpening is new. To the best of our knowledge, this work is the first study on LUT-based pan-sharpening.\n2. The proposed method is fast, and it can process 4K-resolution images in under 1 ms using a single NVIDIA GeForce RTX 2080 Ti GPU.\n3. The approach is simple, and its computational efficiency is outstanding.\n\nWeaknesses:\n1. The proposed method focuses on computational efficiency, which may come at the cost of achieving the highest performance.\n2. Some parts of the paper lack clarity. For example, the meaning of \"OOM\" in Table 1 is not explained.", "was_revised": true, "final_justification": "The author has addressed all the issues I was concerned about, so I have decided to increase the score.", "rebuttal": "Thank you for your review.\n# Weaknesses1\n\nOur Pan-LUT is specifically designed to efficiently process high-resolution remote sensing images under resource-constrained conditions, even if this focus on computational efficiency may come at the cost of achieving peak performance. Its lightweight architecture enables practical deployment, effectively bridging the gap to real-world applications.\n\n# Weaknesses2\n\n\"OOM\" in Table 1 refers to “Out of Memory”. We will revise the table caption and related descriptions for better clarity.\n\n# Question1\n\nWe independently evaluate the inference time of each LUT module with different sizes $M$ to assess their computational efficiency, where $M$ denotes the number of bins per dimension. Additionally, we evaluate the SDLUT module equipped with the Rotation Ensemble Strategy (RES) for comparison. All experiments are conducted using input images with a resolution of $4096 \\\\times 4096$.\n| Module | M=3 | M=6 | M=9 | M=17 | M=33 |\n| :---: | :-: | :--: | :--: | :---: | :---: |\n|| Inference (ms) | Inference (ms) | Inference (ms) | Inference (ms) | Inference (ms) |\n| AOLUT | 0.0677 | 0.1078 | 0.1247 | 0.2451 | 0.2988 |\n| SDLUT + RES | 0.2127 | 0.2569 | 0.3275 | 0.7142 | 0.9564 |\n| SDLUT | 0.0469 | 0.0685 | 0.1015 | 0.1474 | 0.2063 |\n| PGLUT | 0.1027 | 0.1179 | 0.1345 | 0.2046 | 0.2779 |\n\nFrom this table, we observe that the RES operation contributes to the majority of the inference time, as any operation on high-resolution images tends to be computationally expensive.\n\n# Question 2\n\nWe evaluate the maximum image resolution that our model can handle on different GPUs.\n\n(e.g. '1K' denotes an image size of $1000 \\\\times 1000$ pixels.)\n\n| Models | 2080Ti (11G) | 3080Ti (12G) | 3090 (24G) | A40 (46G) | A800 (80G) |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n||resolution|resolution|resolution|resolution|resolution|\n| Pan-LUT-1 | 8.2K | 8.5K | 11.1K | 16.2K | 20.2K |\n| Pan-LUT-2 | 7.5K | 7.7K | 9.4K | 12.4K | 14.2K |\n| Pan-LUT-3 | 5.6K | 5.7K | 7.3K | 10.1K | 11.6K |\n\nOur method is sufficiently lightweight to process 4K to 8K resolution images on a single NVIDIA 2080Ti GPU. \n\n# Question 3\n\nWe demonstrate the advantages of the Rotation Ensemble Strategy (RES) from both theoretical and empirical perspectives.\n\n**Reducing Memory Usage.**\nThe RES effectively increases the receptive field of SDLUT from $2 \\\\times 2$ to $3 \\\\times 3$. Without RES, achieving the same receptive field would require increasing the LUT dimensionality from 4 to 9, leading to a memory complexity of $O(N^9)$. In contrast, RES enables a larger contextual receptive field without increasing LUT size, thereby reducing memory usage.\n\n**Local Texture Enhancement.**\nLet $(w, h)$ denote the pixel located at the w-th column and h-th row of the image.\nWe provide a proof that RES extends the receptive field from $2 \\\\times 2$ to $3 \\\\times 3$.\nIdeally, we aim to incorporate all pixels within the local $3 \\\\times 3$ region centered at $(w, h)$, corresponding to the input index set:\n\n$G_{tgt} = \\\\{(w{-}1,h{-}1), (w,h{-}1), (w{+}1,h{-}1), (w{-}1,h), (w,h), (w{+}1,h), (w{-}1,h{+}1), (w,h{+}1), (w{+}1,h{+}1)\\\\}.$\n\nBy default, SDLUT focuses on the bottom-right neighborhood of each pixel.  The input index group is defined as\n\n$G_{r0} = \\\\{(w,h), (w+1,h), (w,h+1), (w+1,h+1)\\\\}.$\n\nWith the proposed **RES**, we apply SDLUT across three rotated versions of the input, effectively capturing additional pixel neighborhoods:\n$G_{r90} = \\\\{(w,h), (w{-}1,h), (w,h{+}1), (w{-}1,h{+}1)\\\\}$,\n\n$G_{r180} = \\\\{(w,h), (w{-}1,h), (w,h{-}1), (w{-}1,h{-}1)\\\\}$,\n\n$G_{r270} = \\\\{(w,h), (w{+}1,h), (w,h{-}1), (w{+}1,h{-}1)\\\\}$.\n\nThen, we can derive the following equation:\n\n$G_{r0} \\\\cup G_{r90} \\\\cup G_{r180} \\\\cup G_{r270} = G_{tgt}.$\n\n**Definition:** w/o RES' means the framework without the Rotation Ensemble Strategy.\n| Method | WV2 | GF2 | WV3 |\n| :---: | :---: | :---: | :---: |\n| Pan-LUT-1 | 40.6362 | 43.6559 | 29.6213 |\n| Pan-LUT-1 (w/o RES)| 40.4993 | 43.2147 | 29.3354 |\n| Pan-LUT-2| 40.8419 | 44.5545 | 29.7929 |\n| Pan-LUT-2 (w/o RES)| 40.7247 | 44.3754 | 29.6786 |\n| Pan-LUT-3| 41.1362 | 44.8559 | 30.1213 |\n| Pan-LUT-3 (w/o RES)| 40.9988 | 44.6672 | 30.0455 |\n\nWe find that RES not only contributes to performance improvement but also significantly reduces the number of parameters."}, {"review_id": "8yuBCpLAWw", "reviewer": "Reviewer_cahF", "summary": "This paper introduces Pan-LUT, a novel learnable Look-Up Table (LUT) framework designed for pan-sharpening. The core contribution lies in the development of a computationally efficient approach that balances performance with resource constraints, particularly for deployment in scenarios where real-time processing is crucial. The experimental results demonstrate that Pan-LUT achieves competitive performance, particularly in terms of computational efficiency, when compared to existing pan-sharpening methods. Specifically, the proposed method exhibits significantly faster inference times than deep learning-based approaches, while maintaining a reasonable level of accuracy.", "questions": "1、The authors should provide a more detailed analysis of the memory complexity. This should include a breakdown of the memory requirement of each component of the proposed method, such as the PGLUT, SDLUT, and AOLUT modules.\n2、Further evaluation of additional Pan-LUT variants (e.g., Pan-LUT-4 and Pan-LUT-5) is necessary to better assess the trade-off between performance and computational efficiency. \n3、How are the hyperparameters λs and λm​ in Eq (11) set? Here, λs controls the smoothness regularization term, while λm​ controls the monotonicity regularization term in the loss function.", "limitations": "N", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths** \\\n1、The primary strength of this paper lies in its introduction of a novel learnable LUT framework for pan-sharpening.\\\n2、The proposed framework achieve a practical balance between performance and efficiency, making it a potentially valuable contribution to the field of remote sensing image processing.\\\n3、Quantitative results demonstrate that the proposed framework substantially outperforms classical approaches. Moreover, it obtains acceptable results with respect to the deep-learning based models, but with a considerably lower processing time.\\\n**Weaknesses** \\\n1、The effectiveness of the proposed method, while reasonable, does not consistently achieve state-of-the-art performance when compared to some deep learning-based methods.\\\n2、The paper lacks illustrative figures that clearly demonstrate the operations of the proposed LUT modules.", "was_revised": true, "final_justification": "The responses have solved all my concerns，so I decide to raise the final score.", "rebuttal": "Thank you for your review.\n# Weakness 1\nOur Pan-LUT is specifically designed to efficiently process high-resolution remote sensing images under resource-constrained conditions. While it does not consistently achieve state-of-the-art performance when compared to some deep learning-based methods, it consistently outperforms other approaches in real-time inference and high-resolution image processing scenarios. Its lightweight architecture enables practical deployment, effectively bridging the gap to real-world applications.\n\n# Weakness 2\nSince it is challenging to visualize operations in 4D or 5D space, we instead provide illustrative figures based on 3D LUTs to clearly demonstrate the structure and operations of the proposed LUT modules. For more details, please refer to the Technical Details section in the supplementary material.\n\n# Question 1\n\n| Module | M=3 | M=6 | M=9 | M=17 | M=33 |\n| :--- | :- | :-- | :-- | :--- | :--- |\n|  | Param (K)  | Param (K)  | Param (K)  | Param (K)  | Param (K)  |\n| AOLUT | 0.97 | 31.10 | 236.20 | 5679.43 | 156541.57 |\n| SDLUT | 0.08 | 1.30 | 6.56 | 83.52 | 1185.92 |\n| PGLUT | 1.21 | 38.88 | 295.24 | 7099.28 | 195676.96 |\n\nThe memory complexities of PGLUT, SDLUT, and AOLUT are $O(5M^5)$, $O(4M^4)$, and $O(4M^5)$, respectively, where $M$ denotes the size of the LUT. As shown in Table above and Figure 5 of the manuscript, increasing the LUT size beyond 9 yields only minor performance gains but significantly consumes excessive storage resources. In all experiments, we use $M=9$ as the default setting.\n\n# Question 2\n\nWe conduct additional experiments to evaluate more Pan-LUT-N variants, where $N$ denotes the number of stacked PGLUT and SDLUT modules. \n\n| Method | WV2 | GF2 | WV3 | Param (M) | Inference (ms) | Inference (ms) |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n||||||$2048\\\\times2048$|$4096\\\\times4096$|\n| Pan-LUT-1 | 40.6362 | 43.6559 | 29.6213 | 0.9579 | 0.3812 | 0.5445 |\n| Pan-LUT-2 | 40.8419 | 44.5545 | 29.7929 | 1.3843 | 0.9434 | 1.0824 |\n| Pan-LUT-3 | 41.1362 | 44.8559 | 30.1213 | 1.8108 | 1.4821 | 1.9648 |\n| Pan-LUT-4 | 41.1624 | 44.9756 | 30.1913 | 2.2373 | 2.0621 | 2.7518 |\n| Pan-LUT-5 | 41.2854 | 45.1733 | 30.2471 | 2.6637 | 2.3712 | 2.9915 |\n| Pan-LUT-6 | 41.4025 | 45.3364 | 30.3087 | 3.0902 | 2.7915 | 3.1024 |\n\nAs shown, deeper models (e.g., Pan-LUT-4 to Pan-LUT-6) offer only marginal improvements in performance compared to shallower variants (Pan-LUT-1 to Pan-LUT-3), while significantly increasing parameter count. The Pan-LUT-4 already contains over 2 million parameters. From a practical application perspective, we consider Pan-LUT-1, 2, and 3 as the default architectural choices.\n\n# Question 3\n\n| Metrics | $\\\\lambda_m$=0 | $\\\\lambda_m$=0.1 | $\\\\lambda_m$=1 | $\\\\lambda_m$=10 | $\\\\lambda_m$=100 |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| PSNR | 29.4287 | 29.4902 | 29.5275 | **29.6213** | 29.3785 |\n\n| Metrics | $\\\\lambda_s$=0 | $\\\\lambda_s$=0.00001 | $\\\\lambda_s$=0.0001 | $\\\\lambda_s$=0.001 | $\\\\lambda_s$=0.01 |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| PSNR | 29.4163 | 29.4176 | **29.6213** | 29.4137 | 29.4044 |\n\nWe tune the hyperparameters $\\\\lambda_s$ and $\\\\lambda_m$ to identify the optimal settings, and find that $\\\\lambda_m = 10$ and $\\\\lambda_s = 0.0001$ yield the best performance. \nA large $\\lambda_s$ (e.g., $>0.0001$) results in worse PSNR, as the smooth regularization limits the flexibility of LUT transformations. In contrast, the PSNR is insensitive to the choice of $\\\\lambda_m$, since monotonicity is an inherent property of LUTs."}]

Author rebuttal:

Meta-review: This paper studies the problem of efficient pan-sharpening in remote sensing. By replacing complex DNN operations with learnable LUTs,  Pan-LUT bridges the gap between computational efficiency and performance in high-resolution pan-sharpening. 

All reviewers recognize the novelty of using a learnable LUT framework and its impact to remote sensing. Compared to previous approaches it enables processing of high-res images in practice, which is a valuable contribution to the field of remote sensing image processing.
Decision: Accept (oral)