Title: Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution
Venue: NeurIPS 2025
Accepted: no
Keywords: Burst Image Super-resolution

Equivariant-CNN

Low-level Computer vision
Abstract: Burst image processing (BIP), which captures and integrates multiple frames into a single high-quality image, is widely used in consumer cameras. As a typical BIP task, Burst Image Super-Resolution (BISR) has achieved notable progress through deep learning in recent years. Existing BISR methods typically involve three key stages: alignment, upsampling, and fusion, often in varying orders and implementations. Among these stages, alignment is particularly critical for ensuring accurate feature matching and further reconstruction. However, existing methods often rely on techniques such as deformable convolutions and optical flow to realize alignment, which either focus only on local transformations or lack theoretical grounding, thereby limiting their performance. To alleviate these issues, we propose a novel framework for BISR, featuring an equivariant convolution-based alignment, ensuring consistent transformations between the image and feature domains. This enables the alignment transformation to be learned via explicit supervision in the image domain and easily applied in the feature domain in a theoretically sound way, effectively improving alignment accuracy. Additionally, we design an effective reconstruction module with advanced deep architectures for upsampling and fusion to obtain the final BISR result. Extensive experiments on BISR benchmarks show the superior performance of our approach in both quantitative metrics and visual quality.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an equivariant convolution-based alignment method for burst image super-resolution. The approach provides consistent transformations between the image and feature domains, enabling pixel-level supervised alignments to be effectively transferred to the feature space. As a result, the proposed method achieves more accurate alignment and improves overall reconstruction performance. Additionally, the reconstruction module is enhanced through the integration of components such as MDTA and INR. The effectiveness of the proposed framework is validated on standard benchmark datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The proposed use of equivariant convolution for alignment addresses a critical challenge in burst image super-resolution (BISR), where accurate alignment significantly impacts reconstruction quality.\n\n- The reconstruction module is enhanced by integrating MDTA and INR, leading to performance improvements.\n\n- The inclusion of theoretical justification for the equivariant design is a strength, lending analytical support to the proposed method.\n\nWeaknesses and Suggestions for Improvement\n\n- While the application of equivariant convolution to BISR appears to be novel, the approach itself mainly adapts existing techniques. As such, the methodological contribution may be seen as incremental rather than innovative.\n\n- The reconstruction module relies on the integration of established components (e.g., MDTA, INR) in an ad hoc manner. This fusion lacks a principled justification and further diminishes the perceived novelty of the work.\n\n- Among the contributions listed at the end of the introduction, the final point refers to empirical performance improvements. Since this is an experimental observation rather than a technical contribution, it does not strengthen the core claims. Thus, the paper essentially presents two main contributions: (1) the application of equivariant convolution to BISR, and (2) a reconstruction module with known techniques. Given this, the overall contribution volume may be considered limited.\n\n- In Equation (1), the transformation function $f(\\cdot)$ is defined as rotation and translation. This raises a question: does the method fail to capture more general transformations such as affine or projective ones? Is invariance to only rotation and translation sufficient for real-world burst frames? If so, further empirical or theoretical justification is needed.\n\n- The paper lacks evaluation using perceptual metrics such as LPIPS or FID, which are especially relevant for assessing visual quality in image generation tasks.\n\n- While the proposed method shows improved quantitative results (e.g., PSNR, SSIM), the qualitative improvements are less convincing. As shown in both the main paper and supplementary material, the visual differences between the proposed method and baselines are subtle and often only apparent when images are heavily zoomed in.\n\n\n- Given that alignment is the core contribution of this paper, relying solely on PSNR and SSIM may be insufficient to evaluate its full effectiveness. For example, recent reference-based SR methods like CSSR include edge-based or structure-aware metrics(such as gradient magnitude similarity deviation (GMSD)) to better capture alignment quality. Incorporating similar evaluations could help highlight the strengths of the proposed alignment module.\n\n  - [CSSR] CSSR: Cross-and Self-feature Transformer with High-Frequency Feature Alignment for Reference-Based Super-Resolution\n  - [GMSD] Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE TIP 2013\n\nQUESTIONS:\nImpact of Frame Distance from Reference Frame:\n- In burst image sequences, the content difference between the reference frame and nearby frames is typically smaller than that with distant frames. Has the model's performance been analyzed in terms of frame distance from the reference? It would be insightful to understand whether the alignment module performs consistently across all frames, or whether distant frames degrade performance due to increased motion or occlusion.\n\nEffect of Number of Input Frames (N = 14):\n \n- The proposed method uses 14 frames as input. Is this number empirically determined to be optimal? Intuitively, increasing the number of frames may improve performance up to a certain point, after which gains are marginal or even negative due to noise, misalignment, or computational overhead. An ablation study or saturation analysis on the number of input frames would strengthen the paper and offer practical insights into the trade-off between performance and computational cost.\n\nLIMITATIONS:\nWhile the proposed method assumes that the transformation between burst image frames can be effectively modeled using rotation and translation, it remains unclear whether this assumption holds in all cases. In real-world scenarios, especially when handheld devices or rolling shutter effects are involved, more complex transformations such as affine or even projective warping may occur. It would be helpful for the authors to clarify whether the model can adequately handle such cases, or to provide empirical justification that rotation-translation equivariance is sufficient for the majority of burst image sequences.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new framework for Burst Image Super-Resolution (BISR) that addresses the limitations of existing alignment methods. Instead of using deformable convolutions or optical flow, the authors introduce an equivariant convolution-based alignment approach that learns spatial transformations in the image domain and applies them consistently in the feature domain. This design improves alignment accuracy in a theoretically sound manner. The framework is further enhanced with a reconstruction module using MDTA blocks and Implicit Neural Representation (INR) for effective feature fusion and upsampling. Experiments on BISR benchmarks show that the proposed method outperforms existing approaches in both quantitative metrics and visual quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The paper introduces a novel use of Equivariant Convolutional Networks (Eq-CNNs) for alignment, allowing spatial transformations learned in the image domain to be applied accurately in the feature domain.\n2. It clearly identifies limitations in existing BISR alignment strategies and provides a well-justified motivation for using Eq-CNNs, supported by theoretical reasoning and intuitive illustrations.\n\nWeakness:\n1. The visualizations are not very informative—in several examples, it is difficult to perceive meaningful differences in image quality, which limits the interpretability of the visual comparisons.\n2. The comparative baseline methods are relatively outdated, with most published before 2023. Including one or two more recent state-of-the-art BISR methods would strengthen the experimental validation.\n3. While the paper reports FLOPs as a measure of efficiency, it would be more convincing to also provide real inference speed (e.g., runtime on GPU) to better reflect practical performance.\n\nQUESTIONS:\n1. Could you please revise the visual comparisons? In their current form, it is difficult to perceive clear differences between methods, which weakens the qualitative evaluation.\n2. Most of the baselines are from before 2023. Would it be possible to include one or two more recent state-of-the-art BISR methods for a more up-to-date comparison?\n3. In addition to FLOPs, could you also report the actual inference time on GPU? This would help better assess the practical efficiency of your method.\n\nLIMITATIONS:\nNo negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses Burst Image Super-Resolution (BISR) by introducing a new alignment framework based on equivariant convolutional neural networks (Eq-CNNs), which enables theoretically principled alignment transformations learned in the image domain to be applied effectively in the feature domain. The authors propose a full pipeline incorporating this alignment scheme, combined with advanced modules for feature upsampling and fusion. Experimental results on standard benchmarks (SyntheticBurst and BurstSR) validate the method’s effectiveness, showing improvements in both quantitative metrics and visual quality over several state-of-the-art approaches. The paper also offers a theoretical analysis supporting the proposed alignment strategy.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \n\n1. Introducing Eq-CNNs into burst super-resolution is novel, as no prior work has explored this direction, demonstrating a certain level of innovation.\n\n2. The paper is clearly written and easy to follow. It provides theoretical justification for how Eq-CNNs enable better alignment, and clearly describes the task setting of burst image super-resolution.\n\n3. The authors conduct thorough ablation and comparative experiments to demonstrate the effectiveness of the proposed method. Additionally, they visualize residuals to show that Eq-CNNs achieve lower reconstruction errors and more efficient feature extraction.\n\nWeakness:\n\n1. Though the approach is theoretically well-founded, the quantitative improvements reported in Table 2 over state-of-the-art methods (e.g., BurstM, BSRT-Large) are quite marginal, particularly on realistic benchmarks like BurstSR, where the PSNR gain is as small as 0.01 or less. Specifically, in Figure 5, the visual enhancement achieved by the proposed method appears limited, and there remains a noticeable color deviation compared to the ground truth, similar to other competing approaches.\n\n2. The content of Proposition 1 largely overlaps with Section 3.3 \"Theoretical Results,\" making it redundant. It is recommended to remove one of them to improve conciseness and avoid repetition.\n\n3. The ablation studies (Table 3, Figures 6/7) are primarily on SyntheticBurst, not the real BurstSR data, making it unclear how robust the alignment strategy is under real-world acquisition artifacts.\n\n4. It is suggested to compare with more recent burst super resolution methods such as QMambaBSR.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper is interesting. Authors propose to estimate the alignment in image domain and apply the alignment in feature domain. As well as a new up-sampling network, new SOTA results are acheived.\n\nSTRENGTHS AND WEAKNESSES:\nA new idea to align in feature domain for burst image SR. \nSimilar idea has been used in other SR tasks.\n\nStrengths\n1. Theoretically Sound Alignment: By leveraging Eq-CNN, the framework ensures consistency between transformations in the image and feature domains. This allows alignment transformations learned via image-domain supervision to be directly applied to features, improving alignment accuracy (as shown by lower feature alignment errors in Table 1 and Figure 2).\n2. Effective Feature Alignment: Compared to optical flow or deformable convolutions, the proposed method reduces feature misalignment, especially on real-world datasets (BurstSR), where it achieves a 1.94 relative error vs. 2.06 for flow-based methods (Table 1).\nAdvanced Reconstruction Module: Integrating MDTA (for capturing global inter-frame correlations) and INR (for multi-scale upsampling) enhances feature fusion and detail recovery, contributing to superior HR reconstruction.\n3. Superior Experimental Performance: On both synthetic (SyntheticBurst) and real (BurstSR) datasets, the method outperforms SOTA methods (e.g., BurstM, BSRT-Large) in PSNR, SSIM, and visual quality, preserving finer textures and reducing artifacts (Tables 2, 4-5; Figures 4-5).\n\nWeaknesses\n1. Limited Transformation Modeling: The framework only models rotation and translation transformations due to the constraints of existing Eq-CNNs. This may not capture complex real-world motions (e.g., non-rigid deformations), limiting applicability in scenarios with intricate misalignments.\n2. Dependence on Eq-CNN Capabilities: Performance is bounded by the equivariance of Eq-CNNs, which currently lack support for more general transformations (e.g., affine or projective transformations).\n3. Potential Overhead in Feature Extraction: Eq-CNNs may introduce additional computational complexity compared to vanilla CNNs, though experiments show the model remains efficient relative to SOTA methods (Table 2).\n4. Limited Generalization to Extreme Degradations: While effective on benchmark datasets, the method’s performance on highly noisy or severely blurred burst frames is not explicitly evaluated, leaving room for further validation.\n\nQUESTIONS:\nWill results be better with sub-pixel alignment?\n\nLIMITATIONS:\nWhat will be the improvments if the movements are small in different burst images ?", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}