Bio-Inspired Image Restoration
Authors: Yuning Cui
Wenqi Ren
Alois Knoll
Venue: NeurIPS 2025 poster ()
Keywords: Image restoration
All-in-one image restoration
Composite degradation
Bio-inspired design
Primary area: deep_learning

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.

Bio-Inspired Image Restoration
Yuning Cuil.?, Wenqi Ren Alois Knoll
Shenzhen Campus of Sun Yan-sen University Technical University of Munich
MoE Key Laboratory of Information Technology
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 degrada- tion handling. Despite these advances, computational efficiency remains critical factor for practical deployment. this work, we present BiolR, an efficient and universal image restoration framework inspired by the human visual system. Specifically, we design two bio-inspired modules, triphheal-to-Foveer (P2F) and Deveal-to-eeripher (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 finegrraiee spatial details through static-to-dynamic two-stage integration strategy. Leveraging the biologically motivated design, BiolR achieves state-of-the-are performance across three repre- sentative image restoration settings: single-degradation, all-in-one, and composite degradation. Moreover, BiolR maintains high computational efficiency and fast inference speed, making it highly suitable for real-worle applications. The code and pre-trained models are available at //gith/bbbtttttt thub. com/ h////cyy///// hom/cyyn/Bioll
Introduction
Image restoration aims reconstruct high-quality images from their degraded counterparts 1 2 3 5 88, which may suffer from quality loss due to adverse weather conditions or the limitations of low-end imaging devices. This task plays critical role supporting various downstream perceptual applications (9] 10, II [2], such as object detection and depth estimation. In the deep learning era, early methods have primarily focused on addressing specific image restoration tasks using Convolutional Neural Networks (CNNs) 13 [4
16 [8] and Transformer architectures [19 Subsequently, numerous general-purpose purpose methods have been proposed tackle variety of tasks using shared or scalable framework, albeit with separately trained instances for each task [ 26 27 28 (2 30 BT). Recently, the concept of all-in-one image restoration has emerged, leading to the development of methods that incorporate degradation-awee -ware priors to handle multiple 41). The latest advancements have further extended this direction by targeting the compusite degradation problem, which involves
[5]
20 21 22
24
degradations within a single model [32 33 34
35|
36
37
38
39,
40
managing multiple degradations simultancously 42
43
44
35|
45 46). A detailed taxonomy of
these developments is presented in Figure Despite these advances, most existing algorithms remain tailored to specific scenarios and struggle to generalize effectively across diverse restoration settings.
In addition to generalization capability, computational efficiency is another critical factor for prac- ticability. To this end, extensive efforts have been devoted to improving the efficiency of image restoration models. For instance, numerous studies have sought to enhance the computational ef- ficiency of Transformer-basedd architectures for high-resolution image restoration by reducing the
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Releeras Nodeie Dograded
BBiiR--S
31.0
BioiR-L
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Madal
MGALCC
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Campenic
27
OKket
BA3024
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Iustase
Inuranoo
10
20 Parameters/M
Parameters.0
Figure 1: Left: Taxonomy of image restoration methods. Middle: Comparison of the number of parameters vs. PSNR on the CDD11 42 dataset for composite degradation image restoration. Right: Comparison of the number of parameters vs. PSNR under the five-task all-in-one setting.
Tirvcll
Eeceptas per man" [=11d]
Perigheral Vison
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Perpher al Visiun
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ZOVER
Distribution of rod and cone photoreceptors across retinal eccentricity. Right: Conceptual overview of foveal -prriphelll interactions in human vision. Due to the distinct spatial distributions and functional specializations of rods and cones, peripheral vision supports coarse scene analysis, whereas foveal vision is responsible for detailed processing. The dynamic interplay between these two systems enables the perception of stable, coherent, and high-fidelity visual experience.
Figure Left:
scope of self-attention operations 226 22
20 In contrast, some methods have adopted 51 31
CNN-based frameworks to achieve high efficiency 30 48 50
6 52, 533, with subset of
these further incorporating frequency-domait processing to expand the effective receptive field. More
recently, a few Mamba-based based image restoration algorithms have been proposed, offering an efficient
mechanism for capturing long-range dependencies [54] 53
56
57
58
59
60
61
62
63 64 65].
Taking into account the two critical factors for practical deployment, universality and efficiency, this paper aims to develop an efficient network for universal image restoration that generalizes well across three representative settings: single-degradation, all-in-one, and composite degradation (see the left part of Figure [a). To this end, we draw inspiration from the remarkable efficiency of the human visual system, whose conceptual overview is illustrated in Figure 2 As shown, the density of cone photoreceptors peaks at the fovea and declines toward the periphery [66], enabling high-acuity detail perception in limited region. In contrast, peripheral vision covers a much larger portion of the visual field, albeit at lower resolution. After the initial encoding of visual information from both foveal and peripheral pathways, foveal signals are extrapolated to support peripheral representations [67]. while peripheral object recognition benefits from integrating large-field field contextual information with foveal vision (68) This interaction is further stabilized through a recalibration process that associates foveal and peripheral inputs into a coherent percept of the visual world.
Inspired by the human perception system, we introduce two bio-inspired modules, termed foveal- to-peripheral (F2P) and peripheral-to-foveer (P2F), to facilitate the interaction between foveal and peripheral visual signals. Specifically, the P2F module delivers large-field contextual signals to local regions by leveraging pixel-to-region affinity. In contrast, the F2P module transmits fine-grained spatial details through a combination of element-wise processing and dynamic integration. The refined foveal and peripheral representations are then recalibrated via element- wise multiplication to enable high-order interactions. Building on these intuitive yet effective designs, we propose BiolR, bio-inspired image restoration model that achieves state-of-the-art performance across three standardized settings: task-specific, all-in-one, and composite degradation, while maintaining high computational efficiency (see Figure J. Notably, BiolR can be viewed as a higher-level general- purpose solution that relates to these three categories in much the same way general architectures

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such as Restormer [25 and NAFNet 30 relate to task- methods, marking further step toward universal and practical image restoration. The main contributions are summarized as follows:
We propose bio-inspired, efficient, universal image restoration network that leverages two specialized modules to facilitate interaction between foveal and peripheral visual pathways.
We introduce the -oveil--o- (F2P) module, which transmits fine spatial details through statictto-dynamie two-stage integration strategy, and the peripheral-to-foveal (P2F) module, which delivers long-range contextual information guided by pixel-to-region affinity,
Extensive experimental results demonstrate that the proposed model achieves state-of-the-at performance across three representative image restoration settings, including four single- degradation tasks on nine datasets, two all-in-one settings, and two composite degradation benchmarks, while maintaining both high computational efficiency and fast inference speed.
2
Related Work
This section reviews related works from two key perspectives that support the practical application of image restoration methods, namely versalility and efficiency.
Image Restoration, Early approaches primarily relied on handcrafted priors to constrain the solution space [7] However, designing effective priors proved to be a non-trivial challenge. Over the past decade, fueled by the rapid progress of deep learning, numerous task-specific methods based on CNNs [3 72 73
75 76 and Transformer architectures have emerged [21] 19 These methods learn generalizable priors from large-scale datasets and have demonstrated superior performance over traditional techniques on various image restoration tasks, such as deraining, dehazing, and deblurring. Despite their success, these task-specific methods often struggle to generalize to other restoration tasks due to their specialized design. To address this limitation, general-purposs architectures have gained popularity, offering strong performance across multiple tasks and sometimes even surpassing task-specific methods on their respective
18
16
52
74
22
20
77
24
6 28 83]. For example, Restormer [25) achieves state-of- the-art results four different tasks through an advanced Transformer architecture. However, these general architectures typically require separately trained instances for each task, forcing users to possess prior knowledge of the degradation type to select the appropriate pre- pre-trained model.
targets [79] 80 [29 48, 31
49 30 26,
81 27
82
To overcome this challenge, recent research has shifted toward all-in-one solutions that can handle multiple degradation types and levels using a single unified model [84] 41
[8 [5] 86 87 88) These methods are typically trained on compound datasets aggregated from several benchmarks and often incorporate degradation-aware priors extracted from the input images. For instance, PromptIR 34 encodes priors into learnable parameters to guide the restoration process. While these models are well-suited for image is affected by only a single type of degradation. However, real-world scenarios often involve composite degradations, where multiple types of impairments co-occur [89 90
platforms, they generally assume that cach input
35
9 92 93 For
example, night photography commonly suffers from both blurring and low-light conditions due to long exposure in dim environments 43) Recently, CDD11 42 was introduced as comprehensive benchmark comprising 11 categories of degradations, including haze, rain, snow, low light, and their combinations. Correspondingly, composite degradation restoration methods have been developed, such as OneRestore [22 that leverages both textual and visual descriptors to handle these complex scenarios.
Despite these advances, existing methods largely target specific problem categories and struggle to generalize across differrntt restoration settings [88 25 34 194. In contrast, model is applicable to three major restoration scenarios, i.e., fingle-dgrradation all-in-one, and composite degradation, by drawing inspiration from the perceptual mechanisms of the human visual system.
Efficient Image Restoration Networks. Efficiency is a critical consideration for practical deploy- ment, enabling faster processing speeds and reduced resource consumption. Recent efforts have focused on improving the computational efficiency of image restoration networks through various strategies. Specifically, several Transformer-basee architectures reduce the computational overhead of self-attention by limiting its scope to smaller regions (27 26 20 such as windows or strips. In addition, some methods incorporate frequency-domain processing to efficiently model long-range
3

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aa) BiolR
Residual Connection
Downsample
Upsample
Single
Convalution operation
BloBlodk
BloBlocks
BioBlocks
BioBlocks
BioBlocks
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Element wise Add/Mul
posiosite
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Matrix multiplicatio
Reshape
Split
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Degraded
Encoder
Decoder
Refinement
Restored
dw33x3
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x x
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x G
dw3333
IW X.C
Norm
1x1
C H x
1X6X6
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BioBlock
[c] BioModule
1xHxW
Figure 3: Architectural overview of the proposed BioIR framework for universal image restoration.
(a) BiolR adopts plain U-shaped architecture, with BioBlock (b) serving as the core building block. (c) BioModule enables dynamic interaction between two types of visual signals through the Peripheral-tee -ooveall (P2F) and loveal-to-Periphe
(F2P) modules. (d) P2F delivers large-field contextual information local regions based on pixel-to-region affinity. (e) F2P propagates fine- grained spatial details by combining dement-wise modulation and dynamic convolution.
dependencies 49
52
95
More recently, Mamba-base based frameworks have been proposed to
achieve global receptive fields through advanced scanning strategies [54] 600 [5 57
59
58 55 96).
On another front, significant progress has been made in model compression and acceleration tech- niques, such as quantization, pruning, and knowledge distillation (97 98 99 100) [01]. which further improve inference efficiency without sacrificing performance. Our proposed model is orthogonal to these techniques, providing strong and efficient baseline that can further benefit from their integration achieve even greater efficiency and speed.
Method
In this section, we first present an overview of the overall pipeline, highlighting the BioBlock as its core component. We then introduce the proposed BioModule, which integrates two key components: the peripheral-to-fover (P2F) module and the foveal-to- sreal-to-perrie to-peripeeral (F2P) module.
3.1 Overall Pipeline
The schematic of the proposed bio-inspired network for universal image restoration is shown in Figure The network adopts a plain U-shaped architecture to efficiently capture hierarchical features, without relying advanced designs such multi-input or multi-output strategies [3I]. As illustrated, consists three main stages: an encoder, a decoder, and a refinement stage, along with two 3 x convolutions used to generate feature embeddings and produce the final residual output, respectively.
More specifically, given a degraded image from single-degradation. all-in-one, or composite degradation setting, the first x 3 convolution embeds the 3-channel input into feature map of size C x W. The encoder then progressively reduces the spatial resolution while expanding the channel dimension, resulting in deep feature map of size 4C x x W The decoder subsequently

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restores the feature resolution back to C' x H x W, with feature-level residual connections to assist the restoration process. The refinement stage further enhances the decoded feature maps [25] 29 34 Finally, the second 3 x 3 convolution produces residual image, which is added to the degraded input via an image-level residual connection to obtain the final restored image.
The three stages are primarily composed of different numbers of BioBlocks, with architectural details illustrated in Figure B(b). Each BioBlock follows a Transformer-styl design but replaces the self-attention mechanism with the proposed BioModule, as shown in Figure 3 c). Given input
features X, the computation within BioBlock can be formulated as follows:
X F()(X)) + XX where X (X((()) +X,
(1)
where X is the output of the BioBlock. Here, V denotes the normalization layer, while B and F represent the BioModule and the feed-forward network (FFN) 225 respectively.
3.2 Bio-Inspired Module (Bio.odulee
Inspired by the human visual system, which processes different types of visual signals through distinet pathways and integrates them to form coherent perception, the proposed BioModule adopts a two-branch design that mimics this biological mechanism. Specifically, the input feature map X RC is first refined using 1 x 1 convolution followed by 3 x 3 depth-wise convolution. Next, two specialized modules, P2F and F2P, are employed to extract complementary feature representations and facilitate their interaction. These features are then recalibrated via element- wise multiplication to integrate the two information streams. Finally, a x 1 convolution produces the output feature map. The overall computation of the BioModule can be formally expressed as:
X P, F = Splin(DWConvxx (Convix1 (X))),
& F2P(P, F)),
(2)
(3)
where DWConv and Cony denote depth- wise convolution and standard convolution, respectively, with the subscript indicating the kernel size. The Split operation evenly divides the feature map along the channel dimension into two parts, denoted as P and F. Notably, the 1 x 1 convolution in Eq. doubles the channel dimension facilitate this splitting operation.
3.2.1 Peripheral-to-Foverr Module (P2F)
The P2F module delivers large-field visual signals to local spatial regions by equipping convolution operations with knowledge of long-range dependencies. To achieve this, it encodes the contextual relationships between each pixel and its surrounding regions by computing the affinity between the pixel and set of regieetreeresentttiie pixels. These affinity values are then integrated through convolutional operations to dynamically generate pixel-specific convolution kernels. This enables the injection of global contextual signals into local regions via Hiding-window convolutions, effectively enhancing local representations with long-range information.
The architectural details of P2F are illustrated in Figure d). Given the input feature map P E RCxxxx average pooling is first applied to extract contextual representations of size C x S x S, which then reshaped to C x 5 for affinity computation. Specifically, the affinity map A is computed between these contextual features and the reshaped feature map P:
A = R(F) @ R(Pool(P)) E HWxS*
where R and Pool denote the reshaping and average pooling operations, respectively, and @ represents matrix multiplication. Each element in the affinity map A encodes the relationship between pixel and specific region. Next, 1 x 1 convolution is applied row-wise to A to generate pixel-specific convolution kernels of size 1 x K2, effectively injecting contextual information into every weight of these kernels by establishing pixel-to-region interactions.
To apply the learned kernels to F, we first perform average pooling on to obtain chamnel-compressed features of size 1 x H x W, improving computational efficiency. These compressed features are then convolved with the learned kernels to produce enhanced features of size x H x W, which are used attention scores to modulate F" resulting in the final output of the P2F module.
5

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3.2.2 Foveal-to-Peripheral Module (F2P)
In contrast to P2F, which focuses delivering global contextual information, the proposed F2P module aims to propagate fine-graines spatial details. As illustrated in Figure F2P employs a static-to-dynamic integration strategy, element-wise modulation followed by dynamic convolution, to fully leverage the detailed local information. Specifically, given input F E R a depth- whith- convolution first applied to extract locally refined representations. These refined features are then used to directly modulate the features through element-wise multiplication, formally expressed as:
= P F, where F DWCoovtx7(F)).
In addition, F is further utilized to generate convolutional kernels for refining P in dynamic, pixel-wise manner. Specifically, F is passed through 1 convolution produce pixel-specific kernels. In parallel, the features P are compressed to a spatial map of size x H x W to enhance computational efficiency. These compressed features are then convolved with the learned kernels to integrate fine- tne-grained information. Finally, the outputs from the element-wiss wise modulation and the convolutional refinement stages are combined via addition, which can be formally expressed as:
== X1 + X2. where
X2 Pool PP) Conv (F).
(6)
where c denotes the convolution operation.
Experiments and Analysis
To validate the effectiveness of the proposed model, we conduct extensive experiments across three image restoration settings: (a) single-degradation, (b) all-in-one, and (c) composite degradation.
Single-degradattin:: The model is separately trained nine datasets covering four restoration tasks, including image desnowing, dehazing, deraining, and low-light image enhancement.
All-in-one: The model is evaluated under two sub-settings: three-task and five-task. It is trained on a compound dataset comprising three or five different tasks and tested on the corresponding test sets, where each image is affected by single type of degradation.
Composite degradation: The model assessed on two benchmarks, LOLBlur 43 and CDD11 42] where images suffer from up to two or three simultaneous degradation types.
We scale the model according to the complexity of the datasets to ensure a balanced trade-off between performance and efficiency. Further details on training configurations and additional results are provided in the supplementary material. In the following tables, the best and second-best results are highlighted in magenta and blue, respectively. FLOPs are calculated on 3 x 256 x 256 input patch.
4.1 Single-Degrrdatton Image Restoration
Image desnowing. We evaluate the proposed model on three widely used datasets for image
desnowing: Snow100K [15), SRRS [102. and CSD (44. As shown in Table [a), our model
significantly outperforms the recent Transformer-bast
architecture MBTF-L V2 [29] on both
Snow100K and SRRS, while requiring lower computational complexity.
Image dehazing. We compare our tiny model with lightweight state-of-the-art algorithms on the SOTS-Indoor (103) dataset for image dehazing. As reported in Table [b),, the proposed BiolR-T achieves a notable performance improvement of 1 1.05 dB in PSNR over the recent Mamba-hased MaIR 159 while using only 39% of its parameters. In addition, we evaluate our base model against heavyweight dehazing methods on the Haze4K [104] dataset. Table [c) further demonstrates the superior performance of our model in this more challenging setting.
Low-light image enhancement. We further evaluate the proposed model on the LOLv2-s 105 dataset for liw-tight image enhancement. The quantitative results, summarized in Table dd), show that our tiny model outperforms the recent Mamba- based enhancer [60] by 0.36 dB PSNR, while using fewer parameters and achieving lower computational complexity.
Image deraining. We first evaluate our model on the raindrop removal dataset AGAN-Data [06] Table Te) shows that our model achieves notable PSNR improvement of 0.66 dB over the recent

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Table 1: Quantitative results on nine datasets across four single-degradation image restoration tasks.
(a) Snowl0OK, SRRS, and CSD for desmowing
(b) SOTS Indoor for dehazing
Snow IOOK
SRRS
CSD
Params (M)
FLOPS
Method
PSNR SSIM Params FLOPs
Method
PSNR SSIM
PSNR
SSIM PSNR
SSIM
(G)
DeHamer
36,63
0.988
132.45
48.93
FocalNetioevas
28
33.53
0.95
31.34
0.98
37.18
0.99
3.74
30.6
Fourmenyuus
4p
37.32
0.990
1.29
20.6
IRNeXUcMLES
48
33.61
0.95
31.91
0.98
37,29
0.99
5.46
42.1
DehazeFormehreg
20
38.46
0.994
4.63
48.64
MBTF-L
SIEI--Vleree Vlgerm
24
33.79
0.95
32.26
0.98
7,43
88.1
OKNet-
S
37.59
0.994
2.40
17.86
ConvIR Besam44
31
31.92
0.96
32.39
0.98
39.10
0.99
8.63
71.2
DEA Netrira
39.16
0.992
2.84
24.88
MBTF-L V2sams
34.01
0.96
32.55
7.29
86.0
59
39.45
0.997
3.40
24.03
BloIR-Bau
34.44
0.96
32,67
0.98
39.19
0.99
7.28
67,81
BloIR-T Dum
40.50
0.997
1.32
16.65
(c) Haze4K for dehazing
(d) LOLv2-s for low-light enhancement
(e) AGAN for raindrop
Method
PSNR SSIM Params FLOPs
Method
PSNR SSIM Params FLOPs
Method
PSNR SSIM
PMNelgccv22
33.49
0.98
81.1
MIRNetaoov20
113
21.94
31.76
785
Restormerevrea
0.934
FSNelwom2
34.12
0.99
13.29
110.5
Restorercevea
25
21.41 0,830
26.13
144.25
IDT [TS]
31,87 0.931
ConvIR IR- BRAMMA
31
34.15
0.99
8.63
71.2
SNR Netevioz: 014
24.14 0.928
4.01
26.35
MAXIMev0622 116
31.87 0.935
Conv Le-aa2
31
34,50
0.99
14.83
129.9
Retinexfomerrcoya
25.67 0.930
1.61
15.57
AWRCPscovs
31,93 0.931
MBTF V Vhnovvs
24
34.47
0.99
7.43
88.1
ManbalRaacvvs
S4
25.55 0.929
4.30
60.66
FProscevvs
118
31.96 0.937
MBIF V2
29
0.99
MamboL Lie
25.87
2.28
20.85
ASTevpes
107
32.32
BiolR-Bon
35.62
0.99
7.28
67.81
BioIR-To
26.22 0.947
1.32
16.65
BioIR-Tom
32.98 0.944
(f) DID-Data and SPA Data for rain streak removal
Table 2: Runtime on NVIDIA RTX 4090 GPU
Method
PSNR SSIM PSNR SSIM (M)
DID-Data SPA Pata
Params ELOPs
(G)
(a)
Single-degradation task
(b) All-in-on one task
Restormerevpat
UlonnetevRe
27
25
35.02 0,9621 46.13 0,9913
35,29 0.9641 47.98 0,9921
50.88
26.13
144.25
45.9
Teck//ooaa
Desnowing METF-L V2
Method
Time'
0,558
PSNR
34,01
Timecs
PSNR
WTTTM 115
DRSformeteveess
NeRD-Rain-Se Rain--
MloRR-Bas..
35.62 0.9668 49.39 0,9933
35,36
35.35 0.9646 48.54 0,9924
0.9623
0.9647 48.90 0.9936
0,9930
33.65
16.41
10.53
7.28
67.81
242.9
79.2
LOLV2-4
Enhance Mam
AGAN
Deraining AST
Saow100K Ours
Ours
0.093
0.085
0.133
34,44
22.98
25.87
26.22
32.32
PrompilR 0.147 29.15 32.06
Ours 0.132 30.99 32.87
McCE-IR
AdalR
0.112
0.144
30.58 32.73
33.69
Table 3: Quantitative results under the three-task all-in-one setting. a indicates the noise level.
Deluzing SOTS
Deraining Rain1ODL
Denoising BSD68
BSD68
BSD68.=m4 PSNR
Average
Method
Params
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
SSIM
PSNR
SSIM
PrompdRsecattt2s
36M
30.58
0.974
36.37
0.972
33.98
0.933
31.31
0.888
28.06
0.799
32,06
0.913
Aro-PromdlRanns
119
35M
30.83
0.979
37.94
0.982
34.06
0.934
31.42
0.891
28.14
0,801
32.49
0.917
UniProcssorecewa
120
1002M
31.66
0.979
38.17
0.982
34.08
0.935
31.42
0.891
28.17
0.803
32.70
0.918
AdalRicuRus
57
29M
31.06
0.980
38.64
0.983
34.12
0.935
31.45
0,892
28.19
0.802
32,69
0
MoCE McCE.IRevees
25M
31.34
0.979
38.57
0.984
34.11
0.932
31.45
0.888
28.18
0,800
32.73
0.917
BioIR-I-cu.
16M
31.69
0.981
38.63
0.984
34.19
0.937
31.54
0.895
28.28
0.809
32.87
0.921
AST 107 algorithm. In addition, we conduct experiments two benchmark datasets, DID-
Data 108 and SPA-Data [T09), for rain streak removal. The results, reported in Table demon- strate that our model outperforms the specialized method NeRD-Rain-S [100] achieving a PSNR gain of 0.49 dB on the real-world SPA-Date [109] while maintaining lower computational cost.
Inference speed comparison. Table 2 a) presents runtime comparisons with leading algorithms on three single-degradation tasks. As shown, the proposed model achieves superior performance while maintaining faster inference speed, highlighting its practical efficiency.
4.2 All-in-One Image Restoration
Three-task setting. We begin by evaluating our model under the three-task all-in-one image restoration setting. The model is trained a compound dataset combining three tasks: dehazing, deraining, and denoising. It is then tested on the corresponding test sets for these tasks. As shown in T our model achieves the best performance across nearly all evaluation metrics, with average PSNR improvement of 0. dB over the second- best method [35). Notably, on the BSD68 [13] dataset for image denoising, different noise levels, while requiring fewer parameters than competing methods.
model achieves substantial performance improvements across three
we further evaluate our model under the five- task all-in-one setting. addition to the three previously considered tasks, we include the GoPro [124] and LOLvI [125] datasets for motion deblurring and low-light enhancement, respectively. As shown
Five-task setting, Following prior works 36
35 37
7

---

19.98 dB Rainy Image
18:40
19.02 dB AdaIR S7
24.74 dB
30.29 BiolR-L
PSN Reference
InstructIR-5 5D 36
McCE-IR 35
Figure 4: Visual results on the Rain 100L 121
dataset under the five-task all-in-one setting,
Table 4: Quantitative comparisons under the five-task setting for all-in-one image restoration.
Dekazing SOTS
Deraining Rain tont.
Denoising RSD68_
Deblurring GoPmo
Low Lig-Light
L.0vv
Avernge
Method
Params
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
CVR22
18
15M
25.24
0.943
35.63
0.965
31.60
0.887
27.87
0.846
21.34
0.826
28.34
0.893
Prompii S2
84
36M
26.54
0.949
36.37
0.970
31.47
0.886
28.71
0.881
22.68
0.832
29.15
0.914
Gridfomen
12
34M
26.79
0.951
36.61
0.971
31.45
0,885
29.22
0.884
22.59
0.831
29.33
0.904
InstructR-SDD... B-SeevA
17M
27.10
0.956
36.84
0.973
31.40
0.873
29.40
0.886
23.00
0.836
29.55
0,908
AdalRqcusss
29M
30.53
0.981
31.35
0.858
23.00
McC'E-IRever
35
25M
30.48
0.974
38.04
0.982
31.34
0.887
30.05
0.899
23.00
0.852
30.58
0.919
BioIR-L-ous
Table 5: PSNR results of directly applying the model pre-trained under the five-task all-in-one setting three denoising datasets: BSD68 [13], Urban100 [27]. and Kodak24 [28
BSD68
[2
Urhun 100 127
Kodak24 [28
25
Average
TransWeathereyeee
129
31.16
29.00
26.08
29.64
27.97
26.08
31.67
29.64
26.74
28.66
IDRevvs: 88
34.78
InstruciRR 5Dacoyas
36
34.00
31.40
28.15
33.77
31.40
28.13
34.70
32.26
29.16
31.44
29.20
AdaIR 37
34.01
31.35
28.06
34.10
31,68
28.29
34.89
32.38
29.21
31.55
BiolR-Loan
34.18
31.52
28.27
34.50
32.16
28.92
35.15
32.67
29.35
31.88
in Table 4 our model achieves the best results in most categories. Notably, establishes a new state-of-the -hrt by outperforming the second-best algorithm, MoCE-IR [35]. with average PSNR improvement of 0.41 dB across all datasets. While MoCE-IR achieves higher scores on the GoPro dataset, this is primarily due its reliance on specialized motion deblurring backbone [126) Additionally, our model surpasses the recent frequency-basee AdaIR (37 by notable margin of 0.79 dB in PSNR. Importantly, these improvements are achieved with fewer parameters, highlighting the efficiency and effectiveness of our bio-inspired design. Visual results in Figure 4 further demonstrate the superiority of our model, showing that its output is significantly closer to the reference image, whereas the first two methods produce results that perform even worse than the degraded rainy input.
Additionally, assess the generalization capability of our model by directly applying it unseen denoising datasets, including Urban 100 [127] and Kodak24 [128). As reported Table our model consistently outperforms recent strong competitors across all evaluation metrics. In particular, it
achieves a notable average PSNR improvement of 0.34 dB over the heavyweight AdaIR [37 further demonstrating its robustness and generalization ability.
Runtime comparison. Table 2bb) reports the inference time of state-of-the-ar all-in-one algorithms on the Roin [12]] dataset. As shown, model achieves significantly higher accuracy than the competing methods under both settings, while maintaining a comparable inference speed.
Discussion. Despite not relying any explicit degradation-relate, priors, as employed in previous methods 34 32 37], our model achieves strong performance in both all-in-one settings. We attribute this success to two key factors: (i) the large-field signals extracted by the peripheral vision branch, which capture the contextual information of degraded images and can serve as implicit priors to guide the restoration of local details in each module: and (ii) the bio-inspired architecture, which endows the model with powerful representation learning capabilities, enabling to recover sharp features from degraded inputs without relying explicit, task-specifi priors.

---

Table 6: Quantitative evaluation on the LOLBlur 43
dataset for composite degradation.
Method
MIMO
NAFNet 330 ECCV22
LEDNet
Restormer 25
RetinexFormer
DarkIR-M CVPR25
DarkRR-L CVPR25
BiolR-T
Venue
ICCV21
ECCV22
CVPR22
ICCV23
Ours
PSNR
22.41
25.36
25.74
26.72
26.02
27.00 0.883
27.30
27.70
SSIM
0.835
0.882
0,850
0.902
0.887
0.898
0.908
Params
Table 7: Quantitative comparison for composite degradation image restoration on the 11 degradation categories of the CDD11 42 dataset. Results are reported in terms of PSNR and SSIM
Method
Params
Rain
Snow (8)
L-H
L.R
H4S
L-H-R
L+HIS
AirNei
9M
24.83
778
24.21
951
26.55
391 26.79
919
23.23
779
22.82
710
23.29
723
22.21
868
23.29
901
21 10
708
22:24
725
23.75
.814
Prompti
26.32
26.10
946
25.05
721
24.51
.61
23.74
752
23.33
347
WGWSNel
26M
21.39
7774
27.90
982
33.15
964
34.43
973
24.27
300
25.06
772
24,60
765
27.23
955
27.65
23.90
772
23.97
.71
26.96
.63
WeltherDiff
8IM
23.58
763
21.99
904
24.85
585
24,80
888
21.83
736
22.69
730
22
707
21.25
868
21.99
868
21.23
716
21.04
698
22.49
799
OneResttre
6M
26,48
32,52
990
33.40
964
34,31
25.79
799
789
957
30.21
24.78
788
791
.878
McCE-IR-S
27.26
32,66
BilIR-
1M
27.14
834
34,77
992
34.48
570
36.25
999
26.11
330
26.17
308
26.01
304
31.04
968
31.00
972
25
22
799
25
802
29.52
8817
BloIR-
7M
27.52
838
36.95
995
35.35
974
37,28
981
26.79
835
26.78
817
26.03
312
32.77
923
33.39
977
26.20
811
26.28
811
30.54 883
Table 8: Ablation results for the proposed bio-inspired components and alternative design choices.
Ablation stadies for individual components.
(b) Peripheral scope (SS.. Fig. B
(c) Feveal vision scope (dw, Fig.
Recalibration P2F F2P
PSNR
FLOPs
Params
Scope
PSNR
FLOPs
Params
Kernel
PSNR
FLOPs
Params
33.18
15.15
1.31
36.10
14.37
1
34.49
14.53
1.24
33.67
14.34
1.23
2 x 2
36.27
14.38
1.23
3
35.32
14.66
1.24
36.64
15.38
1.25
36.20
1.26
36.96
15.61
1.29
8 8
36.45
14.62
1.23
x
36.59
15.33
1.28
38.26
16.65
1.32
16
x
16
36.64
15.38
1.25
9
36.83
15.87
1.30
4.3 Composite Degradation Image Restoration
Two-degradation setting. We first evaluate model on the LOLBlur 433 dataset, which presents a composite degradation scenario involving both low-light conditions and blur. T that model significantly outperforms the recent specialized method DarkIR-L 44 by 0.4 dB, while requiring only 10% of its parameters. Compared to DarkIR-M. our model achieves an even larger improvement of up to 0.7 dB in PSNR, while using less than half the number of parameters.
Three-degradation setting. We further compare our model against state-of-the-art multi-task image restoration methods on the more challenging CDD11 42 dataset, which involves composite degradations across 11 degradation categories, with each image affected by up to three simultaneous degradation types. As reported in Table 7 our base model, BiolR-B, consistently achieves the best performance across all evaluation metrics, significantly outperforming the second-best method, MOCE-IR-S, by an average margin of 1.02 dB in PSNR. Notably, our tiny model, BioIR-T, surpasses most competing methods on the majority of metrics while using only approximately 1M parameters, highlighting the efficiency of our design.
4.4 Ablation Study
We conduct ablation studies by training the dehazing model on the RESIDE-Indoor for 100k iterations and evaluating its performance on the corresponding SOTS-Indoor More ablation study results are provided in the supplementary material.
103
dataset
103 test set.
Effects of individual components. Table 8 presents the results of adding the proposed compo- nents baseline model, which consists of only two 1 convolutions and 3 depth-wise convolution within the BioModule. This baseline achieves 33. 18 dB on the SOTS-Indoon [03] dataset. Introducing feature splitting and the recalibration mechanism improves the performance by 0.49 dB, while slightly reducing computational overhead. Adding the two key components, P2F and F2P, boosts performance by 3.46 dB and 3.78 dB over the baseline, respectively, demonstrating the effectiveness of the proposed bio-inspired design. Finally, applying recalibration to the outputs of both P2F and F2P yields the best result, achieving 5.08 dB PSNR improvement over the baseline with only negligible increase in computational cost.
glement-wiss multiplication

---

Peripheral vision scope. The peripheral vision branch in P2F provides large-field contextual signals support foveal processing. We investigate the effect of varying the peripheral vision scope, which is controlled by the spatial resolution 5 of the pooled feature map (Figure A larger S corresponds smaller peripheral field. shown in ()), increasing generally improves performance, suggesting that more focused peripheral scope benefits the model. In our final design, we adopt resolution 16 x 16, which provides a favorable trade-off between performance and efficiency.
Foveal vision scope. The foveal vision branch in F2P delivers fine-grained spatial information to the peripheral pathway. As shown in Table X c). expanding the spatial extent of foveal processing consistently improves performance, but at the expense of increased computational cost. This observa- tion aligns with the biological organization of the human visual system, where the separation and cooperation of foveal and peripheral pathways are essential for achieving both stability and efficiency perception. Based on this trade-off, we adopt kernel size of in our final model achieve balanced compromise between accuracy and computational efficiency.
5
Conclusion
This study presents an efficient and universal image restoration network inspired by the human visual system. Specifically, the proposed peripheral-to-foveal (P2F) module extracts large-field contextual signals and integrates them into local regions to guide the restoration process, while the foveal-to- peripheral (F2P) module propagates fine- -re-irai local features to enhance peripheral representations. recalibration mechanism further harmonizes these two complementary information streams. Built upon this simple yet highly effective bio-inspired design, the proposed 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.
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grants 6231 13310666 62322216, U24B20175, the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A15 11155111339 and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University under Grant 231gbj015.
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A Technical Appendices and Supplementary Material
A.1 Implementation Details
This subsection presents the implementation details for the three image restoration settings. To accom- modate the varying complexities of different datasets and scenarios, we provide three architectural variants of our model: BioIR-T - (Tiny), BioIR-B (Base), and BioIR-L (Large). As summarized in Table these variants are scaled by adjusting the number of channels and the number of BioBlocks at each resolution level, while keeping all other architectural components unchanged across versions.
degradation image restoration. Following previous methods B1] [8] the proposed model trained using the I loss computed both the spatial and frequency domains, optimized with the Adam optimizer. The initial learning rate is set to 10-3 and is progressively reduced to x 10-7 using a cosine annealing schedule. Consistent with prior Transformer-baser approaches [25]. our models are generally trained for 300K iterations. For deraining tasks, evaluation is performed on the Y channel in the YCbCr color space, following standard protocols [107 21 110.. All experiments are conducted using PyTorch on NVIDIA Tesla A100 80G GPUs.
Single-dgraaaatio//compsii
Table 9: The configurations of different BioIR variants.
BiolR
Channels
BioBlocks
Parameters
FL.OPs
Tiny (T)
[32, 64, 128, 128, 64, 32, 32]
[1, 1, 2, 2, 1, 1, [3. 6, 6, 3, 3,
1.32M
16.65G
Base (B)
[48, 96, 192. 192, 96, 48, 48]
7.28M
67.81G
Large (L)
[48, 96, 192, 192. 96, 48, 48]
[6. 6, 14, 14, 6.
15.85M
125.64G
All-in-one image restoration. The datasets used in the two all-in-one image restoration settings are summarized in Table The three-task setting includes dehazing, deraining, and denoising, while the five-task setting additionally incorporates motion deblurring and low-light image enhancement for both training and evaluation. Noisy images are synthesized by adding Gaussian noise with {15,2 25, 50) to clean images. Our dataset configurations closely follow those adopted in previous all-in-one studies
35 37 36].
Following (35). the proposed model is trained using dual-domain l loss functions on 128 x 128 image patches. The batch size is set to 32, and the model is trained for 100 epochs in the three-task setting and 150 epochs in the five-task setting. The initial learning rate is set to 2 x 10 For data augmentation, random horizontal and vertical flips are applied.
Table 10: Datasets used in the experiments for two all-in-one settings.
Tisk
Dehazing
Deraining
Denoising
Deblurring
Enhancement
Training set
RESIDE-2 3 103
Rain 2
BSD400 100
WED
H
GoPro
LOLvi LOLvI
P5
SOTS-Ouldeor
Rain1ODL
12
BSD68
Urban100
127
Kodak24
GoPro
A.2
Additional ablation studies
Table 11: Additional ablation studies for F2P. The kernel size of the depthwise convolution in F2P is
set to 7 x 7.
Method
PSNR
FLOPs
Params
F2P (one-stage, G=3 in Fig. 3))
35.85
15.33
1.28
F2P (two-stage, G=3)
36.59
15.33
1.28
F2P (two-stage, G=5)
36.96
15.61
1.29
F2P (twa-stage, G=7)
37.08
16.02
1.31
More ablations for F2P. We first conduct experiments to validate the effectiveness of the two-stage
design in the F2P module. As reported in Table IT the one-stage variant, obtained by removing the element-wise multiplication, achieves a PSNR of 35.85 dB, while the proposed two-stage design improves performance 36.59 dB without introducing additional computational overhead. We
24

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further investigate the impact of the kernel size (G in Fig. B(e)) used in the dynamic convolution. The results show that performance consistently improves as the kemnel size increases. Based on this trade-off between computational cost and accuracy, we select G = 5 in our final model.
More ablations for P2F. Similarly, investigate the impact of increasing the kernel size (K in Fig. d)) of the dynamic convolution in the P2F module. The results, summarized in Table [2] show performance trends across different kernel sizes. To maintain lightweight design, we adopt K in our final model.
Table 12: Additional ablation studies for P2F. Ss 16 in P2F.
Method
PSNR
FL.Oss
Params
P2F (K=3 in Fig. d))
36.64
15.38
1.25
P2F (K=5)
36.8
17.2
1.30
A.3 Evaluation on perceptual metries
In addition to PSNR and SSIM 132,, this subsection reports evaluation results using the perceptual metric LPIPS [33 further assess the effectiveness of the proposed model. [able/[3comppel our model with leading all-in-one algorithms. As shown, our model achieves the best LPIPS scores on most datasets, demonstrating superior perceptual quality compared to competing methods.
Table 13:
Comparisons under the five-task (top) and three-task (bottom) all-in-one image restoration (lower is better).
settings, evaluated in terms of LPIPS 133
Dehazing SOTS
Deraining Rain
Denoising (BSD68)
Deblurring GoPro
Low-Light LOLv!
Method
15
25
50
AdRIR
37
0.0129
0.0139
0.0634
0.1114
0.2105
0.1902
0.1206
McCF-IRevERz
35
0.0126
0.0137
0.0610
0.1029
0.1945
0.1444
0.1183
BioIR-L ouns
0.0107
0.0107
0.0569
0.1015
0.1934
0.1450
0.1122
AdaIR
37
0.0116
0.0118
0.0611
0.1079
0.2112
MoCE-IReVPR2S
0.0119
0.0106
0.0575
0.1008
0.1906
BiolR-Lous
0.0110
0.0108
0.0509
0.0924
0,1859
A.4 Additional visual results
This subsection presents additional qualitative results for single-degradation, all-in-one, and composite degradation image restoration tasks, organized as follows:
Single-degradationn Figure (SRRS [102] desnowing), Figure 6
(Haze4k 104
dehazing),
Figure (LOLv22s [005]. enhancement)
Composite degradation: Figure
8 (LOLBlur
Figure 9 (CDD1I
42
Figure 10
(Real-
LOLBlur 43
All-in-one: Figures 11
12
100 121 BSD68 123 five-task setting)
These results further demonstrate the superior capability our model in addressing various types of degradations across diverse image restoration scenarios.
A.5 Limitation and Broader Impact
This work presents bio- bio-inspired image restoration network that achieves state-of oate--of-the-ar performance across three representative restoration settings, while maintaining high computational efficiency. However, the human visual perception process is highly complex and remains an active area of inves- tigation 688 [34]. The bio-inspired mechanisms proposed in this study represent only a preliminary attempt to mimic this process. Additionally, the current recalibration strategy is implemented using simple element-wise multiplication. Designing more advanced recalibration mechanisms may further improve performance and robustness, which we consider promising direction for future work.
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While this study focuses on the academic advancement of universal image restoration, the proposed algorithm holds potential for various practical applications. It may bring positive social impacts, such as enhancing image quality captured under adverse conditions or with low-end devices. However, it also raises potential concerns, including the risk of privacy leakage when enhancing sensitive content. Nonetheless, we believe the benefits significantly outweigh the risks, and users are encouraged to apply additional pevacy-preserving techniques to safeguard sensitive information in images.
26

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17.17 dB Snowy Image
26,40 dB ConvIR-B 31
29.18 dB MBTF V2 29
31.83 dB BioIR-B
PSNR/+00 Reference
Figure 5: Visual results on the SRRS 102 dataset for image desnowing. The bottom-right sub- images present the Lı error maps computed between the restored and reference regions enclosed by small red boxes, where brighter pixels indicate larger errors, highlighting the superiority of our approach.
11.35 dB Hazy Image
21.81 dB FSNet 79
27.90 dB
30.22 dB BiolR-B
PSNR/+00 Reference
MBTF V2 29
Figure 6: Visual results on the Haze4k 104] dataset for image dehazing. The bottom-right sub-images present the L1 error maps computed between the restored and reference regions enclosed by small pink boxes, where brighter pixels indicate larger errors, highlighting the superiority of our approach.
9.41
13.06
17.37
19.83
29.20 dB
PSNR/<<
10.62 dB
17.82 dB
21.63 dB
24.14 dB
30.06 dB
PSNR/+3
10.49 dB Low-light Image
16.50 dB MIRNet
25. 13 dB Retinexformer
26.30 dB MambaLLIE
27.18 dB BioIR-T
PSNR/+X Reference
Figure 7: Visual comparison on the LOLv2-s 105] dataset for low-light image enhancement.
7.44 dB Degraded Image
20.66 dB LRDNet 4B
17.15 dB
26,26 dB
PSNR/+b6 Reference
DarkIR-I. 44
BiolR-T
43 dataset under composite degradations. Our model achieves effective brightness restoration and detail recovery from low-light and blurred inputs.
Figure 8: Qualitative results on the LOLBlur
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15.07 dB
37.82 dB
38.34 dB
42.60 dB
PSNR/+<
16.15
25.52
26.40 dB
32.56
PSNR/+00
14.59
31.05 dB
32.31 dB
32.72 dB
PSNR/<0
15.29 dB
26.99 dB
25.71 dB
30.66 dB
PSNR/+00
13.34 dB Degraded Image
30.55 dB OneRestore 47
30.73 dB
32.49 dB
PSNR/+<< Reference
MoCE-IR-S 35
Ours
Figure 9: Visual results on the CDDII 42
dataset for composite degradations.
JL
O
O
4
616
00
616
100
616
00
Low-light Blurry Image
DarkIR-L L 44
BiolR-T
Figure 10: Generalization evaluation by directly applying the model pretrained on LOLBlur 4 to real-world night blurry images. The top example is from the RealBlur [90] dataset, while the bottom example is captured in an uncontrolled real-world setting. Our model more effectively restores both text details and overall brightness.
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26.29 dB Rainy Image
38.74 dB InstructIR-5D
40.37 dB AdaIR
40.37 dB MoCE-IR
42.28 dB BioIR-L
PSNR/+00
Reference
14.72 dB Noisy Image
30.96 dB Instruct|R-95
31.08 dB AdaIR
30.77 dB MoCE-IR
31.37 dB BioIR-L
PSNR/+00 Reference
Figure 11: Visual results under the five-task all-in one setting.
29

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14.92 dB Noisy Image
26.49 dB InstructIR-5D
26.53 dB AdaIR
26.56 dB MoCE-IR
26.96 dB BiolR-L
PSNR/+00 Reference
14.43 dB Noisy Image
32.67 dB Instruct 5D
32.66 dB
AdaIR
32.54 dB MoCE-IR
32.94 dB
PSNR/+00 Reference
BioIR-L
Figure 12: Visual results under the five-task all-in- one setting.
30


Reviews:
[{"review_id": "CF0YWDt8FD", "reviewer": "Reviewer_imoR", "summary": "The 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.", "questions": "Please check the weakness section. \nIt seems to be typo in table 9 where base and large model seems to have the same channels, blocks.", "limitations": "The current major limitation for me is the novelty of the work, which does not seem to be properly addressed in the work.", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strenghts:\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).", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank reviewer imoR for the thoughtful comments and constructive suggestions. We address the issues point by point below.\n\n$\\colorbox{pink}{Q1.1}$  *bio-inspired modules*:\n\nPlease refer to our response to Q3.1 for a detailed explanation of the module design.\n\n\n\n$\\colorbox{pink}{Q1.1(a)}$  *splitting operation*:\n\nThe features $P$ and $F$ denote the peripheral and foveal signals (or branches), respectively. It is important to clarify that this distinction does not arise from a splitting operation; rather, $P$ and $F$ are conceptually differentiated by the subsequent processing applied to them. For instance, in the P2F branch, spatial average pooling ($C \\times H \\times W \\rightarrow C \\times S \\times S$) is used to extract peripheral visual information.\n\nIn our model, the splitting operation generates two feature sets that are subsequently processed to obtain the peripheral and foveal vision signals. This operation serves two purposes: (1) improving efficiency by applying peripheral and foveal processing to only half of the features, and (2) enhancing feature diversity through interactions between the two halves. To validate this design choice, we perform an ablation study without splitting, where both vision signals originate from the same features. As shown in Tab. 1, these variants perform worse than our original design while also incurring higher computational complexity.\n\n**Table 1: Ablation studies for the splitting operation.**\n|Method|PSNR|FLOPs|#Params|\n|----------|----------|----------|----------|\n|P2F (ours) | 36.64| 15.38 | 1.25M |\n|P2F (w/o splitting)| 35.39 | 16.2| 1.33M |\n|F2P (ours) | 36.96 | 15.61 | 1.29M |\n|F2P (w/o splitting) | 36.92 | 17.67 | 1.43M  |\n\n$\\colorbox{pink}{Q1.1(b)}$ *average pooling*:\n\nThe use of pooling techniques offers two main advantages: (1) reducing spatial or channel dimensions, which lowers computational overhead and contributes to the model’s efficiency while preserving competitive performance through our architectural design; and (2) enabling large receptive fields via spatial pooling in P2F, effectively mimicking peripheral vision. As shown in Table 8b, decreasing the pooling factor in P2F improves performance but also increases FLOPs.\n\nFor deblurring tasks, we conduct an additional experiment on the ultra-high-definition (UHD) motion deblurring dataset, UHD-Blur [2], a challenging 4K benchmark that stresses both performance and efficiency. We train our model following the configuration of the recent CVPR 2025 method, ERR [4]. We set the number of BioBlocks in our tiny model to [1,1,1,1,1,5] for fair comparison. As shown in Tab. 2, our model outperforms the strong competitor ERR by a significant margin of 0.68 dB in PSNR, despite having fewer parameters. This result underscores the effectiveness of our overall design choices.\n\n**Table 2: Results on UHD-Blur for UHD motion deblurring.**\n|Method | PSNR | SSIM | #Params|\n|----------|----------|----------|----------|\n|FFTformer [1] | 25.41| 0.725| 16.6M|\n|UHDFormer [2]|  28.82 |0.844 |0.339M|\n|UHDDIP [3]  |29.51| 0.858 |0.81M|\n|ERR [4] | 29.72 |0.861 |1.131M|\n|Ours |30.40  | 0.875 | 0.88M|\n\nAs suggested, we individually replace average pooling with max pooling in FP2 and F2P. As shown in Tab. 3, this replacement results in a performance drop in both modules. This decline is likely due to the fact that average pooling preserves signal patterns within the pooled regions by aggregating information, whereas max pooling retains only the strongest activations. Nonetheless, the models using max pooling still outperform the baseline, highlighting the effectiveness of our overall design.\n\n**Table 3: PSNR scores when replacing average pooling with max pooling in P2F (at two positions) and F2P (at one position).**\n|Method | PSNR | \n|----------|----------|\nBase |33.18|\n|Base+P2F|36.64 (avg.)/35.08 (max)|\n|Base+F2P|36.96 (avg.)/36.59 (max)|\n\n$\\colorbox{pink}{Q1.1(c)}$ *extrapolation operation*:\n\nIn the biological visual system, the eye can focus on only one location at a time, and foveal signals are often extrapolated to interpret peripheral regions. In contrast, deep learning models are capable of learning dedicated patterns for all regions simultaneously. Therefore, our network does not strictly rely on extrapolating learned foveal signals to peripheral areas. Rather, the perception process of the visual system is introduced primarily for illustrative purposes, and our architecture is designed conceptually to emulate its underlying mechanisms.\n\nTo allow the fine-grained spatial signals from foveal vision to benefit peripheral representations, we adopt a two-stage design. In this design, each pixel in the foveal branch is propagated forward either through element-wise multiplication or by generating integration weights based on each pixel. If one were to draw an analogy, the relationship between the pixel-wise weights from the foveal vision and the integrated peripheral region could be loosely interpreted as a form of extrapolation.\n\n$\\colorbox{pink}{Q1.2}$ *more ablation studies*:\n\nAs suggested, the additional ablation results are presented in Tab. 4. We first replace our recalibration method (multiplication) with simple alternatives, including addition and channel concatenation followed by a convolution layer (to adjust channel dimensions). However, neither alternative outperforms our design in terms of accuracy or computational efficiency. We further explore more complex variants, such as cross-attention and recursive gated convolutions (g$^n$conv) [5]. While these achieve better performance, they incur significantly higher computational overhead, which conflicts with our goal of developing an efficient and effective module. This trade-off is further highlighted by our final model outperforming both variants in terms of PSNR, FLOPs, and parameter count.\n\n**Table 4: Alternatives to the recalibration operation.**\n|Method | PSNR | FLOPs| #Params|\n|----------|----------|----------|----------|\n|Mul.(Ours)  |  33.67 | 14.34 |1.23M |\n|Add  | 33.28 | 14.34 | 1.23M|\n|Concat+CNN | 33.65 |15.97 | 1.39M |\n|Cross-att. | 35.98 | 16.76 | 1.55M |\n|g$^n$conv [5] | 35.47 | 18.27| 1.60M |\n|Our Final | 38.26 | 16.65 | 1.32M|\n\nRegarding the dynamic convolution in P2F, the method for generating attention weights plays a critical role. In our model, these weights are derived from a pixel-to-region affinity map. To assess the impact of this design, we conducted an additional experiment in which the attention weights were generated by directly adjusting the channel count of the peripheral features via a convolution layer. This alternative approach achieved only 34.15 dB in PSNR, which is significantly lower than the 36.64 dB obtained with our original design. This comparison highlights the importance of the attention weight generation strategy in the effectiveness of the dynamic convolution.\n\n$\\colorbox{pink}{Q1.3}$ *typo*:\n\nSorry for the typo. The correct number of BioBlocks in the large model is [6,6,14,14,6,6,4]. We will fix it.\n\n>Reference\n\n[1] Efficient frequency domain-based transformers for high-quality image deblurring, CVPR23.\n\n[2] Correlation matching transformation transformers for uhd image restoration, AAAI24.\n\n[3] Ultra-high-definition restoration: New benchmarks and a dual interaction prior-driven solution, arxiv24.\n\n[4] From zero to detail: Deconstructing ultra-high-definition image restoration from progressive spectral perspective, CVPR25.\n\n[5] HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions, NeurIPS22."}, {"review_id": "dwYnS89K5D", "reviewer": "Reviewer_rjfQ", "summary": "The 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.", "questions": "The 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\"?", "limitations": "Conceptual–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.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "The authors have sufficiently addressed the concerns and limitations I raised, and I am updating my score to Accept based on their responses.", "rebuttal": "We sincerely thank Reviewer rjfQ for the valuable time and thoughtful feedback. We respond to your comments point by point below.\n\n$\\colorbox{pink}{Q2.1}$  *more explanations for the modules (question 1)*\n\nInspired by the hierarchical processing of the human visual system, we loosely organize our model into three functional stages: encoding, interaction, and recalibration [1]. While these stages do not strictly map to anatomical counterparts, they reflect a coarse functional analogy: early encoding extracts spatial features, intermediate interaction enables complementary integration of foveal and peripheral information, and final recalibration adjusts feature responses based on their mutual relevance. To implement these concepts, we utilize fundamental deep learning components. Specifically, we employ downsampling and depth-wise convolution to extract large-scale contextual signals and refine local features, respectively. Next, we introduce dynamic mechanisms to integrate the learned representations, inspired by the adaptability of biological receptive fields modulated by stimuli. Finally, we apply a simple element-wise multiplication for feature recalibration, leveraging its simplicity, parameter efficiency, and capacity to model non-linear dependencies between the two streams.\n\nOn the other hand, we acknowledge inherent differences between the biological visual system and deep learning architectures. For instance, the human visual system typically focuses on a single position at a time, whereas deep models can learn patterns across the entire feature space simultaneously. To reflect this distinction, our model learns a pixel-wise affinity map in the P2F module and a pixel-wise attention weight in the F2P module. Furthermore, we visualize the features after spatial pooling in P2F and depth-wise convolution in F2P; the former captures large-scale semantic information, while the latter retains fine-grained spatial details. Due to conference policy, we are unable to update the manuscript or provide an external link at this stage, but we will include these visualizations in a future version of the paper.\n\nPlease refer to our response to Q3.1 for further explanation of the module design.\n\nAdditionally, we explicitly acknowledge in the Limitation section that human visual perception is highly complex and remains an open research area. Our proposed architecture represents a preliminary and conceptual attempt to mimic certain aspects of this process. We agree that a more rigorous theoretical analysis would further strengthen our work, and we consider this an important direction for future research.\n\n$\\colorbox{pink}{Q2.2}$ *justification for results (question 2)*\n\nWhile we do not claim theoretical optimality, we believe that the model’s effectiveness can be attributed to its architectural bias: (1) a vision system-inspired three-stage mechanism forms the foundation of our design; (2) the interaction and recalibration stages serve as efficient information fusion modules, effectively complementing feature representations and selectively enhancing those that are most relevant to the task; (3) the dynamic affinity-based integration introduces adaptability that enhances generalization across varying degradations, and (4) the overall design strikes a deliberate balance between effectiveness and efficiency. For example, as shown in Tab. 5, a more advanced recalibration module yields slightly better performance but introduces significantly higher computational overhead, exceeding that of our final model, despite its inferior overall score. Moreover, the effectiveness of our approach is supported by extensive ablation studies on architectural choices and hyperparameter settings.\n\nOverall, our model incorporates several key components commonly found in advanced architectures, including multi-scale representation learning, dynamic feature integration via affinity map generation, and efficient interactions between foveal and peripheral representations. Together, these elements contribute to the model’s strong performance across three major image restoration tasks. We also emphasize that our training configurations strictly follow prior works to ensure fair comparisons. To support reproducibility and future research, we will release the code and pre-trained models.\n\n$\\colorbox{pink}{Q2.3}$ *bio-inspired? (question 3)*\n\nOur use of the term *bio-inspired* is intended to reflect inspiration from the mechanisms observed in the human visual system, particularly the cooperative roles and interactions between high-acuity (foveal) and broad-context (peripheral) processing, rather than to imply a strict biological or anatomical replication. The proposed modules aim to abstract key ideas such as hierarchical encoding, adaptive integration, and efficient modulation, which are broadly aligned with how biological vision balances efficiency and precision. While our current design does not model detailed physiological processes, we believe the bio-inspired terminology remains appropriate in the context of mechanism-level inspiration.\n\nNonetheless, we understand the importance of precision in terminology, and we will revise the manuscript to better clarify this framing and avoid potential misinterpretation.\n\n$\\colorbox{pink}{Q2.4}$ *sophisticated recalibration mechanism*\n\nOur original intention behind the recalibration design was to recalibrate the two branches while maintaining high efficiency. We acknowledge that more sophisticated strategies may further improve performance. As you suggest, we first replace our recalibration method with simple alternatives, including addition and channel concatenation followed by a convolutional layer (to adjust channel dimensions). However, neither alternative outperforms our design in terms of accuracy or computational efficiency. We further explore more sophisticated variants, such as channel-wise cross-attention and recursive gated convolutions (g$^n$conv) [2]. While these achieve better performance, they incur significantly higher computational overhead, which conflicts with our goal of developing an efficient and effective module. This trade-off is further highlighted by our final model outperforming both variants in terms of PSNR, FLOPs, and parameter count.\n\n**Table 5: Alternatives of the recalibration operation.**\n|Method|PSNR|FLOPs|#Params|\n|-|-|-|-|\n|Mul.(Ours)  |  33.67 | 14.34 |1.23M|\n|Add  | 33.28 | 14.34 | 1.23M|\n|Concat+CNN | 33.65 | 15.97 | 1.39M |\n|Cross-att. | 35.98| 16.76 | 1.55M|\n|g$^n$conv [2] | 35.47 | 18.27 | 1.60M|\n|Our final model | 38.26 | 16.65| 1.32M|\n\n$\\colorbox{pink}{Q2.5}$ *unknown degradations*\n\nAs suggested, we evaluate the pre-trained model in the five-task all-in-one setting on out-of-distribution tasks, including defocus deblurring and desnowing. We compare our results with AdaIR (ICLR 2025), which explicitly incorporates degradation-aware frequency priors, and the MoE-based MoCE-IR (CVPR 2025). As shown in Tab. 6, our model outperforms these strong baselines across most evaluation metrics while maintaining superior parameter efficiency. Although AdaIR and MoCE-IR use sub-networks to extract degradation-related priors for distinguishing degradation types, these sub-networks still struggle to generalize to unseen datasets. In contrast, our model incorporates dynamic mechanisms, such as affinity generation, that enhance its representational capacity in the all-in-one setting. Therefore, our model demonstrates strong potential as a baseline that could further benefit from incorporating degradation-aware priors to achieve even better performance.\n\n**Table 6: PSNR and SSIM scores for out-of-distribution evaluation.**\n|Method|DPDD (defocus deblurring)|SRRS (desnowing)|#Params|\n|-|-|-|-|\n|AdaIR [3]|15.49/0.601 | 21.86/0.845| 29M|\n|MoCE-IR [4] |15.58/0.598 |21.82/0.838 | 25M |\n|Ours | 17.80/0.629|21.93/0.841 | 16M|\n\n>Reference\n\n[1] A review of interactions between peripheral and foveal vision, Journal of vision, 2020.\n\n[2] HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions, NeurIPS22.\n\n[3] Adair: Adaptive all-in-one image restoration via frequency mining and modulation, ICLR25.\n\n[4] Complexity experts are task-discriminative learners for any image restoration, CVPR25."}, {"review_id": "MOF1WyrrRN", "reviewer": "Reviewer_AN93", "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.", "questions": "1. Explain why P2F and F2P are bio-inspired. The interpretability of feature interactions is not sure.\n2. The visual comparisons are mainly from synthetic degraded images. Real world captured images are preferred to show the generalization of BioIR.", "limitations": "See Questions.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "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.\n2. 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.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer AN93 for recognizing the strengths of our method and for the constructive feedback. We address the raised issues point by point below.\n\n$\\colorbox{pink}{Q3.1}$ *more explanations*\n\nWe explain our bio-inspired design on two levels. The first level mimics the perception process of the human visual system through a three-stage mechanism, as illustrated in Figure 2. The second level involves the design of specific modules inspired by key functional characteristics of the visual system, rather than a one-to-one mapping to biological structures. The following parts describe how these functional mechanisms are emulated in our model design.\n\n**(a)** Unlike the human visual system, which can focus on only one position at a time, deep learning models are capable of learning patterns across the entire feature space. Accordingly, we learn pixel-wise affinity maps and attention weights in the P2F and F2P modules, respectively.\n\n**(b)** In the retina, cone cells are concentrated around the fovea to capture fine-grained details, while rod cells cover a much larger area to support contextual peripheral vision. Cone cells, in contrast to rod cells, require more ganglion cell connections and lead to increased computational overhead in the brain. As a result, increasing the size of the foveal region or reducing the peripheral region leads to higher computational complexity.\n\nTo mimic this variation, we apply spatial pooling to extract contextual signals in the P2F module and perform matrix multiplication after pooling. This design ensures that the complexity is proportional to $S^2$ (downsampled spatial size), which is inversely related to the size of the peripheral region. Hence, the complexity of affinity generation in P2F decreases as the peripheral region increases. In contrast, in F2P, we directly generate attention weights after a depth-wise convolution, making the computational cost proportional to the kernel size. Tables 8(b) and 8(c) illustrate how the complexity varies with scope/kernel size in both modules.\n\n**(c)** To simulate the bidirectional interaction between foveal and peripheral vision, we employ dynamic convolution in both modules, enabling content-aware and input-adaptive visual integration. This design is inspired by the adaptability of biological receptive fields, which are modulated by external stimuli.\n\n**(d)** In F2P, since foveal vision focuses on extracting fine-grained local features, we integrate two complementary mechanisms: dynamic convolution and element-wise multiplication. Dynamic convolution enables content-dependent, spatially adaptive transformations, analogous to high-level semantic modulation in the human visual system. In contrast, element-wise multiplication serves as a low-level gating mechanism or gain control. By summing the outputs of both pathways, the model is able to fully exploit the valuable fine-grained information provided by the foveal stream.\n\n\n$\\colorbox{pink}{Q3.2}$ *generalization evaluation*\n\nThank you for the helpful comment. We provided the visual comparisons for real-world low-light deblurring in Figure 10 of the paper. However, due to the conference policy, we are currently not permitted to update the manuscript or include an external link to provide additional real-world visual results. As an alternative way to demonstrate the generalization capability of our model, we apply the model pre-trained under the five-task all-in-one setting to previously unseen tasks, including defocus deblurring and desnowing. As shown in Tab. 7, our model outperforms recent strong all-in-one approaches on most metrics while maintaining high parameter efficiency.\n\n**Table 7: PSNR and SSIM scores for out-of-distribution evaluation.**\n|Method | DPDD (Defocus deblurring) | SRRS (Desnowing) | #Params |\n|-| -| - | - |\n|AdaIR [1] |15.49/0.601| 21.86/0.845| 29M|\n|MoCE-IR [2] |15.58/0.598 |21.82/0.838 | 25M |\n|Ours |17.80/0.629|21.93/0.841 | 16M |\n\n>Reference\n\n[1] Adair: Adaptive all-in-one image restoration via frequency mining and modulation, ICLR25.\n\n[2] Complexity experts are task-discriminative learners for any image restoration, CVPR25."}, {"review_id": "NiM2tD7Jsb", "reviewer": "Reviewer_jF93", "summary": "The 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.", "questions": "- 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.", "limitations": "The 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "Most of my concerns are addressed.", "rebuttal": "We sincerely appreciate the reviewer's insightful comments and the encouraging recognition of our work. Below, we address the raised concerns point by point.\n\n$\\colorbox{pink}{Q4.1}$  *more explanations  (weakness 1/question 1)*\n\nPlease refer to Q3.1.\n\n$\\colorbox{pink}{Q4.2}$  *terminology*\n\nWe will make the revisions accordingly. To further demonstrate the universality and generalizability of our model, we conduct additional evaluations on domain-specific tasks and previously unseen datasets.\n\nSpecifically, we conduct experiments on ultra-high-definition (UHD) image restoration (4K), medical image restoration, and remote sensing image restoration. We compare our model with several recent algorithms within each individual domain. Tables 8 to 12 show that our model extends effectively to these domain-specific image restoration tasks, outperforming existing methods while maintaining high parameter efficiency. For UHD image restoration, we set the number of BioBlocks in our tiny model to [1,1,1,1,1,1,4] for fair comparison.\n\n**Table 8: Results on UHD-Blur for UHD motion deblurring.**\n|Method|PSNR|SSIM|Params/M|\n|-|-|-|-|\n|FFTformer[1]|25.41|0.725|16.6|\n|UHDFormer[2]|28.82|0.844|0.339|\n|UHDDIP[3]|29.51|0.858|0.81|\n|ERR[4]|29.72|0.861|1.131|\n|Ours|30.40|0.875|0.88|\n\n**Table 9: PET image synthesis on PolarStar M660.**\n|Method|PSNR|SSIM|Params/M|\n|-|-|-|-|\n|DRMC[5]|36.00|0.935|0.62|\n|Restore-RWKV-light[6]|36.96|0.943|1.16|\n|Ours-T|37.17|0.946|1.32|\n\n**Table 10: CT image denoising on AAPM.**\n|Method|PSNR|SSIM|Params/M|\n|-|-|-|-|\n|DenoMamba[7]|33.53|0.915|112.62|\n|Restore-RWKV-light[6]|33.64|0.918|1.16|\n|Ours-T|33.74|0.919|1.32|\n\n**Table 11: MRI image super-resolution on IXI.**\n|Method|PSNR|SSIM|Params/M|\n|-|-|-|-|\n|F-UNet[8]|31.26|0.931|32.12|\n|Restore-RWKV-light[6]|31.36|0.931|1.16|\n|Ours-T|31.50|0.933|1.32|\n\n**Table 12: Remote sensing dehazing results (PSNR/SSIM) on SateHaze1k. The dataset contains three levels of haze, and separate models are trained for each level.**\n|Method|Thin|Moderate|Thick|Params/M|\n|-|-|-|-|-|\n|Trinity[9]|22.65/0.896|24.73/0.934|20.57/0.824|-|\n|FocalNet[10]|24.16/0.916|25.99/0.947|21.69/0.847|3.74|\n|FMambaIR[11]|24.58/0.912|25.83/0.939|22.65/0.850|4.20|\n|Ours-T|24.62/0.922|27.46/0.938|22.98/0.850|1.32|\n\nIn addition, to further demonstrate the generalizability of our model, we apply the model pre-trained in the five-task all-in-one setting to previously unseen tasks, including defocus deblurring and desnowing. Tab. 6 shows that our model outperforms strong all-in-one methods across most evaluation metrics.\n\nRegarding the term *universal image restoration*, we conduct experiments across three major tracks of image restoration tasks. Compared to MoCE-IR (*any image restoration*), our evaluation additionally covers nine datasets for single-degradation settings and an extra composite degradation benchmark. Furthermore, in contrast to works on *universal image restoration* such as [12], we include both single-task and composite degradation scenarios. That said, we will consider revising our manuscript to avoid overstating our contributions.\n\n$\\colorbox{pink}{Q4.3.1}$  *orange bounding box*\n\nThe bounding box surrounding Instances A to D indicates that our model can be applied to multiple image restoration tasks using different model copies, highlighting the universality of our approach.\n\n$\\colorbox{pink}{Q4.3.2}$  *references*\n\nIn the Introduction and Related Work sections, we provide a broad discussion of prior works related to universality and efficiency, interwoven with concrete examples of relevant methods. Given the broad applicability of our method across multiple scenarios, we review related works in these contexts to highlight the wide-ranging utility of our approach. However, we will consider providing a more focused and selective review in a future revision.\n\n$\\colorbox{pink}{Q4.3.3}$  *typo*\n\nWe will fix them.\n\n$\\colorbox{pink}{Q4.3.4}$  *venues*\n\nWe included the venues to highlight the competitive performance of our approach relative to recent methods, particularly those from less familiar tracks. However, as suggested, we will remove them in the revised version.\n\n$\\colorbox{pink}{Q4.3.5}$  *gray color*\n\nAs suggested, we will either use different colors to distinguish these functions or remove color usage entirely.\n\n$\\colorbox{pink}{Q4.3.6}$  *bar chart*\n\nWe greatly appreciate your helpful suggestion and will consider adopting this format in a future version.\n\n$\\colorbox{pink}{Q4.4}$  *more ablation studies for modules (question 2)*\n\nIt is worth noting that our training configurations strictly follow those of previous methods, without introducing any additional tricks. For instance, the dual-domain loss function has been widely adopted in the image restoration field in recent years, across both single-degradation and all-in-one settings, as evidenced by references [1,26,27,29,33,46,47] in the paper. As suggested, removing the FFT loss leads to a 0.74 dB decrease in PSNR (from 33.67 dB to 32.93 dB).\n\nIn P2F, the strategy for generating attention weights is crucial. In our model, these weights are derived from a pixel-to-region affinity map. To evaluate the impact of this design, we conducted an additional experiment where the attention weights were instead generated by directly adjusting the channel dimensions of the peripheral features via a convolution. This alternative approach yielded a PSNR of only 34.15 dB, notably lower than the 36.64 dB achieved with our original design. This result underscores the importance of the attention weight generation strategy in the effectiveness of P2F.\n\nIn F2P, we adopt a static-to-dynamic strategy. As shown in Table 11 of the paper, we have conducted an ablation study by removing the static part. Here, we further evaluate the effect of removing the dynamic component instead. This variant yields a PSNR that is 1.27 dB lower than our model (36.96 dB), highlighting the critical contribution of the dynamic component to overall performance.\n\nFor ablations for the recalibration mechanism, please refer to Tab. 5.\n\n$\\colorbox{pink}{Q4.5}$  *ablation studies in all-in-one*\n\nIt is worth mentioning that our baseline is built upon Restormer, which is also used as the baseline model in previous all-in-one methods, including AdaIR, Art-PromptIR, and PromptIR. In addition, our training configuration closely follows prior works such as PromptIR, AdaIR, and MoCE-IR, without incorporating any additional tricks. We will release the code and pre-trained models.\n\nAs suggested, we conduct ablation studies under the three-task setting due to the limited time available during the rebuttal period. Tab. 13 presents the averaged results obtained by removing each proposed module from the full model. Removing F2P results in a 0.15 dB drop in PSNR, whereas removing P2F, which transfers large-scale contextual information to local regions, leads to a more pronounced performance degradation.\n\n**Table 13: Ablation studies under the three-task all-in-one setting.**\n|Method|PSNR|SSIM|\n|-|-|-|\n|w/o P2F|32.51|0.919|\n|w/o F2P|32.72|0.920|\n|Full | 32.87 | 0.921|\n\nDespite not employing any explicit degradation-aware priors, our model achieves competitive results in all-in-one settings. It does, however, incorporate dynamic mechanisms, such as affinity map generation in P2F and attention weight generation in F2P. Overall, our model serves as a strong baseline and can be further enhanced by integrating explicit priors.\n\n$\\colorbox{pink}{Q4.6}$  *large kernel in F2P*\n\nWe leverage the proposed modules to emulate foveal and peripheral vision, extracting fine-grained spatial details and broader contextual information, respectively. Both modules benefit from larger convolutional kernels. We believe this phenomenon aligns with the human visual system: it would be advantageous if the fovea could perceive larger fine-detail regions. However, compared to the rod cells in peripheral vision, cone cells in foveal vision require more ganglion cell connections and incur greater computational overhead in the brain. As a result, increasing the number of cone cells would lead to significantly higher processing complexity than that of rod cells. Moreover, since \"large\" and \"small\" are relative concepts, we conduct ablation studies using different kernel sizes in P2F and F2P to determine an appropriate configuration that balances accuracy and computational cost.\n\n$\\colorbox{pink}{Q4.7}$  *limitation*\n\nWe will expand the discussion of our limitations to include additional cases of potential negative societal impacts.\n\n>Reference\n\n[1] Efficient frequency domain-based transformers for high-quality image deblurring, CVPR23.\n\n[2] Correlation matching transformation transformers for uhd image restoration, AAAI24.\n\n[3] Ultra-high-definition restoration: New benchmarks and a dual interaction prior-driven solution, arxiv24.\n\n[4] From zero to detail: Deconstructing ultra-high-definition image restoration from progressive spectral perspective, CVPR25.\n\n[5] Drmc: A generalist model with dynamic routing for multi-center pet image synthesis, MICCAI, 2023.\n\n[6] Restore-rwkv: Efficient and effective medical image restoration with rwkv, IEEE Journal of Biomedical and Health Informatics, 2025.\n\n[7] DenoMamba: A fused state-space model for low-dose CT denoising, arxiv24.\n\n[8] Fourier Convolution Block with global receptive field for MRI reconstruction, Medical Image Analysis, 2025.\n\n[9] Trinity-net: Gradient-guided swin transformer-based remote sensing image dehazing and beyond, IEEE Transactions on Geoscience and Remote Sensing, 2023.\n\n[10] Focal network for image restoration, ICCV23.\n\n[11] FMambaIR: A hybrid state space model and frequency domain for image restoration, IEEE Transactions on Geoscience and Remote Sensing, 2025.\n\n[12] ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration, arxiv23.\n\n[13] HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions, NeurIPS22."}]

Author rebuttal:

Meta-review: The paper received mostly positive feedback after the authors provided their rebuttal. 

Initially, the reviewers expressed several main concerns, including a disconnect between the concept and its implementation, insufficient rigorous justifications, potential information loss due to pooling, unclear details regarding extrapolation, ambiguous terminology, inconsistent framing, and inadequate relevant ablation studies. The authors' rebuttal and additional experiments addressed most of these concerns. After the rebuttal and subsequent discussion, no further issues were raised. 

Reviewer imoR, who had initially given a "borderline reject" rating, noted in the follow-up discussion that "the authors made a good rebuttal and addressed most of my concerns," and expressed a willingness to "increase the score from borderline reject to borderline accept."

The area chair concurs with the reviewers' overall evaluations and recommends accepting the paper.
Decision: Accept (poster)