Title: Bio-Inspired Image Restoration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image restoration

All-in-one image restoration

Composite degradation

Bio-inspired design
Abstract: Image restoration aims to recover sharp, high-quality images from degraded, low-quality inputs. Existing methods have progressively advanced from task-specific designs to general architectures, all-in-one frameworks, and composite degradation handling. Despite these advances, computational efficiency remains a critical factor for practical deployment. In this work, we present BioIR, an efficient and universal image restoration framework inspired by the human visual system. Specifically, we design two bio-inspired modules, Peripheral-to-Foveal (P2F) and Foveal-to-Peripheral (F2P), to emulate the perceptual processes of human vision, with a particular focus on the functional interplay between foveal and peripheral pathways. P2F delivers large-field contextual signals to foveal regions based on pixel-to-region affinity, while F2P propagates fine-grained spatial details through a static-to-dynamic two-stage integration strategy. Leveraging the biologically motivated design, BioIR achieves state-of-the-art performance across three representative image restoration settings: single-degradation, all-in-one, and composite degradation. Moreover, BioIR maintains high computational efficiency and fast inference speed, making it highly suitable for real-world applications.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes BioIR, aimed to build a generalizable and efficient model for image restoration. Specifically, BioIR is a U-shaped network that replaces self-attention with two biologically-inspired operators: Peripheral-to-Foveal (P2F), which routes large-field context to local features via pixel-region affinities, and Foveal-to-Peripheral (F2P), which sends fine spatial details back to the peripheral stream through a static-to-dynamic integration. BioIR reaches state-of-the-art PSNR/SSIM across single-degradation datasets, all-in-one benchmarks and composite-degradation datasets.\n\nSTRENGTHS AND WEAKNESSES:\nThe work is technically solid: the operators are carefully engineered, ablations are thorough, and experimental coverage is broad (single, unified and composite degradations). Unlike prior works that specialize in a single setting, BioIR demonstrates strong performance on all three image restoration paradigms, despite not relying on explicit degradation priors. BioIR is also shown to be lightweight and fast. Visualizations and architectural figures are clear and helpful in conveying the design intuitions.\n\nThat said, several aspects could be improved. \n\n1. while the biological analogy provides a good narrative, the exact correspondence between the proposed modules and actual biological mechanisms feels largely functional/metaphorical rather than mechanistic. The observed performance gains could plausibly be attributed to other architectural features (which are also not employed by previous methods, such as richer cross-scale fusion and the dual-domain loss function), rather than specifically to bio-inspiration. Also, the math around the proposed modules requires more intuition (see \"Questions\"). \n2. the writing suffers from some misuse of terminology and inconsistent framing. Note that terms like “generalizability” (Line 32), “universality” (Line 42), and “versatility” (Line 78) have nuanced difference, and their interchanged use here blurs important distinctions. \"Universality\" focuses more on covering a wide range of existing scenarios, yet \"generalizability\" implies the adaptability to any unseen scenario (which can be totally artificial). These terms should be applied more precisely. Additionally, the claim of \"universal\" image restoration is somewhat overstated, as typical tasks like super-resolution and JPEG deblocking are not addressed.\n3. Apart from these, there are a few minor weaknesses (mainly on the presentation) that detract from the paper:\n   - The orange bounding box in the left part of Figure 1 is confusing. What does it mean?\n   - Although NeurIPS does not limit the number of references, there are too many references for introducing the background and related work (CNN-, Transformer-, Mamba-like models are already well-known in 2025). Many citations are only superficially discussed. Please consider providing a more focused and selective review, so the readers feel less confused when tryinig to track specific claims in the paper. \n   - In Lines 145--146, The order of $\\mathcal{F}$ and $\\mathcal{B}$ are reversed ($\\mathcal{F}$ should point to the FFN). \n   - In Tables 1--3, I suggest removing the publication venues/subscripts for each baseline method, as they clutter the tables without adding meaningful information.\n   - The use of gray highlighting is inconsistent across tables. In Tables 1, 2, 5, and 6, gray boxes are used to highlight the results from BioIR models. But in Tables 3, 4 and 7, the boxes are marked gray to convey different meanings (some boxes even take no color, see the last column of Table 3). In Table 8, the gray color seems to indicate the best option in the ablation study. This will confuse readers.\n   - Given the complexity of the proposed BioModule, an efficiency-performance bar chart (like Figure 2 in the paper: \"A ConvNet for the 2020s.\") would help distill practical insights more clearly.\n\nQUESTIONS:\n- Could the authors provide more insight into the design rationale behind the different operations? Specifically, what guides the choice between matrix multiplication (Eq. 4), element-wise multiplication (Eq. 5), and convolution + addition (Eq. 6)? Are these design decisions intended to reflect distinct biological processes? If that is the case, what do they reflect, respectively?\n- Some components (e.g., the dual-domain loss, the different types of feature interaction in BioModule) might play a foundational role in enabling the rest of the architecture to function effectively.  Have the authors investigated how the performance of P2F and F2P changes when these components are ablated?\n- I am also interested in the root cause for the all-in-one image restoration capability of BioIR (without explicit degradation priors). The authors hypothesize that the peripheral branch implicitly captures degradation priors by extracting large-field signals (Lines 263–265). Unfortunately, this lack supporting experimental results. Is there an ablation study that follows the setting in Table 4 and reveals the core design that unlocks the strong all-in-one capability? Or is such capability already presented in the baseline model?\n- Table 8(c) suggests that larger receptive fields benefit not only the P2F (which aligns with its contextual role) but also the F2P module, which is intended to capture fine-grained details. This seems counterintuitive. Could the authors explain why F2P benefits from larger kernels, and whether this deviates from the biological analogy?\n\nA clearer clarification (would be better if with experimental analysis and visualization) on the connection between the biology background and the designed BioModule, as well as a thorough ablation study on the all-in-one image restoration capability, would help enhance the impact of this work and increase the rating.\n\nLIMITATIONS:\nThe paper addresses standard limitations, but the discussion around potential negative societal impacts (e.g., misuse of restoration models for manipulating images in sensitive contexts) is not explicitly covered and could be expanded. Additionally, a more explicit treatment of failure cases, particularly on unexpected or compound degradations, would be beneficial.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

comments: SUMMARY:
This paper presents BioIR, a novel bio-inspired image restoration framework that emulates the functional interplay between foveal and peripheral pathways in the human visual system. The proposed architecture incorporates two key modules: the Peripheral-to-Foveal (P2F) module, which leverages pixel-to-region affinity to propagate large-field contextual signals, and the Foveal-to-Peripheral (F2P) module, which employs a static-to-dynamic integration strategy to enhance fine-grained spatial details. Extensive experiments across single-degradation, all-in-one, and composite degradation settings demonstrate that BioIR achieves state-of-the-art performance while maintaining high computational efficiency.

STRENGTHS AND WEAKNESSES:
1. BioIR outperforms existing methods on multiple benchmarks (e.g., PSNR=31.69  on SOTS-Indoor for dehazing and  PSNR=23.29 on LOL for low-light enhancement) with less parameters. The SOTA methods are the latest with many visual comparisons provided.
2. Peripheral-to-Foveal (P2F) and Foveal-to-Peripheral (F2P) modules in BioModule is a contribution of this paper, which synergistically combine global context and local detail refinement. However, it should be explained clearly why P2F and F2P are bio-inspired. In the network architecture, it can only be observed that P2F and F2P are interacted dual-stream.

QUESTIONS:
1. Explain why P2F and F2P are bio-inspired. The interpretability of feature interactions is not sure.
2. The visual comparisons are mainly from synthetic degraded images. Real world captured images are preferred to show the generalization of BioIR.

LIMITATIONS:
See Questions.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces BioIR, an efficient and universal image restoration framework inspired by the human visual system. Unlike prior task-specific or degradation-aware methods, BioIR generalizes across single-degradation, all-in-one, and composite degradation settings, while maintaining high computational efficiency.\n\nBioIR emulates human vision through two novel modules:\n\nPeripheral-to-Foveal (P2F): Injects large-field contextual signals into local (foveal) regions using pixel-to-region affinity.\n\nFoveal-to-Peripheral (F2P): Propagates fine-grained spatial details outward using a two-stage static-to-dynamic integration strategy.\n\nThe network architecture follows a U-shaped design with BioModules replacing self-attention in Transformer-style blocks. This bio-inspired design promotes dynamic interaction between global and local features and is shown to boost both accuracy and efficiency.\n\nThrough comprehensive experiments, BioIR achieves:\n\nState-of-the-art performance on 9 datasets across 4 single-degradation tasks (e.g., dehazing, deraining, desnowing, low-light enhancement).\n\nSuperior results in 3-task and 5-task all-in-one restoration without explicit degradation priors.\n\nLeading results on composite degradation datasets (e.g., CDD11, LOLBlur), outperforming models with more parameters.\n\nAn ablation study confirms the contribution of P2F, F2P, and the recalibration mechanism, and runtime tests show fast inference on high-resolution images.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nInnovative Bio-Inspired Architecture: The proposed P2F (Peripheral-to-Foveal) and F2P (Foveal-to-Peripheral) modules draw inspiration from the functional interactions in the human visual system. This biologically motivated design introduces a novel perspective on contextual integration and detail propagation in image restoration.\n\nStrong Generalization Across Tasks: BioIR demonstrates remarkable versatility by achieving state-of-the-art performance across three representative image restoration settings: single degradation, all-in-one, and composite degradation. This makes the model suitable for diverse real-world scenarios.\n\nHigh Computational Efficiency: Despite strong performance, the model is highly efficient. The tiny version (BioIR-T) achieves competitive results, significantly reducing inference time and resource requirements compared to existing baselines.\n\nComprehensive Experimental Validation: The model is thoroughly evaluated on 9 datasets covering various degradation types and is benchmarked against strong baselines. Both quantitative and qualitative results support the effectiveness of the proposed method.\n\n## Weaknesses\nLack of Theoretical Justification: While the bio-inspired modules are intuitively motivated, the paper does not provide rigorous theoretical analysis to justify why the proposed interactions between foveal and peripheral features are optimal or fundamentally effective.\n\nSimple Recalibration Mechanism: The recalibration step between P2F and F2P streams relies on element-wise multiplication, which may be overly simplistic. More sophisticated fusion strategies could potentially yield better results.\n\nLimited Adaptivity to Unknown Degradations: The model lacks explicit degradation-type prediction or task-adaptive mechanisms. In contrast, some prior works incorporate degradation-aware priors or task-conditioned prompts.\n\nBiological Analogy Not Empirically Validated: While the architecture is inspired by the human visual system, the paper does not provide empirical or neuroscientific evidence to support the analogy beyond intuitive similarity.\n\nQUESTIONS:\nThe proposed modules are motivated by biological inspiration. However, in practice, the implementation mainly involves computing an affinity matrix, and there is neither qualitative nor quantitative evidence to support whether the modules function as intended. Could the authors provide visualizations or empirical results that demonstrate how the modules behave in line with the claimed design?\n\nMoreover, there appears to be no theoretical analysis explaining why the proposed approach performs better than prior methods. Are there any theoretical insights or analyses that can justify the effectiveness of the design?\n\nLastly, it is still unclear how the proposed modules, described as bio-inspired in the introduction, are truly analogous to biological visual processing. Why did the authors choose to frame the design as \"bio-inspired\"?\n\nLIMITATIONS:\nConceptual–Implementation Gap:\nAlthough the modules are claimed to be biologically inspired, the actual implementation largely reduces to affinity-based feature interactions and element-wise operations. There is a lack of direct evidence—either qualitative visualizations or quantitative evaluations—that these mechanisms faithfully reflect biological perceptual processes.\n\nLack of Theoretical Analysis:\nThe paper does not provide a theoretical explanation for the observed performance gains. Without insights into the model's expressivity, inductive bias, or optimization advantages, it is difficult to understand why the proposed modules outperform conventional designs.\n\nAmbiguity of the \"Bio-Inspired\" Framing:\nThe rationale for describing the architecture as \"bio-inspired\" remains vague. The analogy to foveal and peripheral vision is conceptually interesting but not rigorously defined or validated. The current design might better be described as structurally inspired rather than biologically faithful.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present BioIR, an efficient and universal image restoration framework inspired by the human visual system. Specifically,they design two bio-inspired modules, Peripheral-to-Foveal (P2F) and Foveal-to-Peripheral (F2P), to emulate the perceptual processes of human vision, with a particular focus on the functional interplay between foveal and peripheral pathways. BioIR network achieves state-of-the-art performance across three major categories of image restoration tasks, including nine datasets for four single-degradation tasks, two all-in-one task settings, and two composite degradation benchmarks, while maintaining high computational efficiency and fast inference speed.\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts:\nThe paper is easy to follow.\nThe paper explores the important aspect of efficiency, and shows comparable results with the state-of-the-art image restoration works on almost all the datasets.\n\nWeaknesses:\n1. The main idea of bio-inspired module is not clear, how is it replicating the human visual system and how is it achieving the desired task? The novelty of the work is limited, specifically the 2 modules.\n1a) In Figure 3c, after the splitting of channels via DW Conv how can we conclude which is the Peripheral and which is the foveal signal? If the authors would have shown some feature maps or some more ablations on that part may have bit clarified the splitting.\n1b) Another point of concernn is the pooling operation used in both the P2F and F2P modules, doesn't it leads to loss of information as well, which is important point to be considered in restoration application like deblurring. What would be the conclusion if they would have alternatively use max pooling?\n1c) As mentioned in line 50, that foveal signals are extrapolated to support peripheral representations, how is this extrapolation being done in the proposed module.\n2. The authors should include more relevant ablations, atleast on different recalibration options, different configurations of the F2P and P2F modules (not just changing the kernel size as shown in supp, like why dynamic conv works well).\n\nQUESTIONS:\nPlease check the weakness section. \nIt seems to be typo in table 9 where base and large model seems to have the same channels, blocks.\n\nLIMITATIONS:\nThe current major limitation for me is the novelty of the work, which does not seem to be properly addressed in the work.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}