Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes an alternating reverse filtering network for multi-spectral image fusion. Motivated by classical reverse filtering, the authors develop an unrolling network based on alternating fixed-point iterations and the learnable multi-scale Gaussian kernel module, which is interpretable. Unlike existing model-driven methods, the proposed iterative network avoids the dependency on pre-defined exact priors and its convergence can be guaranteed theoretically. In addition, this paper introduces a new perspective to solving the image fusion problems. Extensive experiments on diverse scenes to thoroughly verify the performance of the proposed method.
strengths_and_weaknesses: Strengths:
1. The authors innovatively propose a new multi-spectral image fusion method without requiring well-designed priors. The exploration of tailoring classic methods for multi-spectral image fusion and combining them with deep networks is worth trying and encouraging.
2. In the image fusion community, the idea that formulating pan-sharpening as an alternately iterative reverse filtering process is interesting. Other researchers could utilize it for further exploration in the field of image fusion.
3. The learnable multi-scale Gaussian kernel module is well designed and the convergence of the iterative process is sufficiently supported by theoretical analysis. 
4. Quantitative and qualitative results clearly verify the effectiveness and scalability of the proposed method.

Weaknesses:
1. More ablation results about the loss functions and iterations should be provided. Please refer to the detailed Questions part.
2. The parameters such as sigma for initializing the Gaussian kernel should be given in line 239.
questions: 1. The structure loss $L_s$ is based on structural similarity, what will happen if loss function $L_s$ uses the L2 loss function?
2. If each alternate iteration output is supervised with losses, would it be better?
3. In the ablation study about the number of iterations K, why does the performance seem to decrease slightly when K reaches 7 compared with K=5? What happens to the model's performance in the case of the larger K? 
4. As far as I know, the GPPNN network compared in the manuscript is also based on an unrolling method. What is the K used in GPPNN here? If the settings of K are different, how to ensure fair comparisons of the two unrolling methods?
5. As mentioned in the Weaknesses, the authors are suggested to provide specific parameters for initializing the Gaussian kernel.
limitations: The limitations and potential negative social impact of their work have been addressed.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The authors propose an alternating reverse filtering network for pan-sharpening. Specifically, a learnable multi-scale Gaussian kernel module is designed to ensure the convergence of the iterative process. The experiments on diverse scenes show that the proposed network achieves better results compared to other pan-sharpening methods.
strengths_and_weaknesses: Strength:
The paper is well-written. The experiments show that the method can perform well in terms of quantitative analysis and qualitative comparison.

Weakness:
1. Originality
The originality of this work is limited. The reverse filtering is derived from [23] and the fusion network is derived from [63]. The description in 3.2, 3.3, and 3.4 sections looks quite similar to [23]. 
2. Clarity
-The multi-scale Gaussian convolution is a group of convolutional layers initialized by 2D Gaussian kernels, but the detailed architecture of these layers is missing. What are the exact architectures of g and f?
-The contribution states that ‘In contrast to existing model-driven methods, our iterative network can obtain HR MS without the need for pre-defined exact priors or assumptions’. It is confusing and more explanations are needed. What does the pre-defined exact prior mean in existing model-driven methods? How does the proposed method obtain HR MS without the need for pre-defined exact prior, compared to prior methods? 
3. Quality
-In Table2, the MSDCNN achieves 0.8251 in QNR and the result of proposed method is 0.8236. However, the result 0.8236 is in bold instead of 0.8251. The proposed method only achieves the best result in one metric.
-Observed from Figure 3, the Brovey method achieves better fusion results in spatial texture compared to proposed method.

questions: How about the parameter numbers and inference time for the proposed network and competing networks?
limitations: Yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The manuscript first formulate the pan-sharpening problem as a reverse filtering process and propose an alternating reverse filtering network (ARFNet) to solve it. Besides, the authors develop multi-scale Gaussian convolution modules to obtain better filtering effect. Experimental results on both simulated and real-world scenes show that the proposed ARFNet outperforms other methods.
strengths_and_weaknesses: Strengths: 
1.	This manuscript combines the deep unrolling model and the reverse filter process, and proposes a novel alternating update deep convolution network for pan-sharpening task.
2.	Experimental results show that this manuscript set up some new state-of-the-art results in both simulated and real-scene datasets.
3.	The writing is clear and easy to understand.
Weakness:
1.	The reverse filter method has been already applied in super-resolution, deconvolution in [1], the Gaussian filter and its convergence condition has also been analyzed. Overall, the novelty is limited.

questions: 1.	As stated in [1], the optimal iteration number is different for variant filters and needs to be set empirically. Is the iteration number set equally in the ablation study of different initialization methods? 

2.	The learned kernels are close to the initialized Gaussian kernels as stated in Line 241, can the learned kernels gain large performance improvement compared with the initialized ones? 

3.	The authors cited the recent method[2], but not compared the experimental results with it.

4.	How about the computation cost and efficiency compared with other methods?

5.	It’s better to include related source citation for the definition, the theorem and reverse filtering formulation.

6.	Please check the subtraction and addition symbols in Fig. 1, they are a bit confusing.

[1] Xin Tao, Chao Zhou, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Zero-order reverse filtering. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 222–230, 2017.
[2] Man Zhou, Xueyang Fu, Jie Huang, Feng Zhao, Aiping Liu, and Rujing Wang. Effective pan-sharpening with transformer and invertible neural network. IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022.

limitations: There is no potential negative societal impact mentioned. Considering the paired dataset is expensive to obtain, the authors could also explore methods in unsupervised manner.
review_title: null

Meta-review: metareview: The paper presents a pan sharpening image fusion approach using deep learning. The overall review sentiment leaned towards accepting the paper. The reviewers appreciated the reformulation of the problem as an iterative reverse filtering process and thought the technique was generalizable, broadening its potential impact. Some concern was mentioned that the paper directly adapts existing techniques, and the theory is directly pulled from those papers. In that sense the paper is applied. The experimental results were convincing to the reviewers in general, which helps justify the mostly applied nature of the paper.
confidence: Less certain
Decision: Accept