Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-01 10:54:46

Abstract: The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an \textbf{U}ncertainty-based degradation representation for blind \textbf{S}uper-\textbf{R}esolution framework (\textbf{USR}). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. Rhe VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach.

Main paper: 
 related work 2.1 blind super-resolution: 
 Contrary to the traditional Single Image Super-Resolution (SISR) task, the objective of blind super-resolution is to reconstruct HR images from their LR equivalents without prior knowledge of the degradation process [33, 44, 60] . Blind super-resolution methods can generally be categorized into the following two types.Model-based SR. This category of SR models the image degradation process. Most methods employ explicit modeling based on Equation (1) , where 𝑘 represents the blur kernel, and 𝑠 denotes the downsampling factor. Within the realm of methods based on blur kernel estimation, IKC [13] introduced an iterative estimation technique and designed a correction function to accurately estimate the blur kernel or degradation features. Utilizing the principle of internal cross-scale recurrence, KernelGAN [5] interprets the maximization of patches within a single image as a problem of data distribution learning and trains a Generative Adversarial Network (GAN) across patches. MANet [27] , a method based on the estimation of spatially variant blur kernels, estimates blur kernels with a network designed to have an optimally sized receptive field. However, these approaches struggle to address degradation modes beyond blur. In the domain of methods based on implicit degradation modeling, DASR [47] and KDSR [53] characterize the image degradation process using contrastive learning and knowledge distillation, respectively. However, due to the lack of effective constraints, DASR and KDSR are unstable and cannot fully extract discriminative degradation representations to guide blind superresolution.EQUATIONLearning-based SR. Learning-based methods endeavor to directly learn degradation patterns from training data in the form of highlevel semantics, foregoing modeling the degradation process [28] . SwinIR [26] , by adopting the Swin Transformer for image restoration tasks, has achieved breakthrough performance. Works such as Restormer [61] , HAT [8] , and DAT [9] further demonstrate the potential of Vision Transformers in low-level visual tasks. Additionally, some researchers have focused on diffusion models [38, 45, 50, 59] , which transform complex and unstable generative processes into several independent and stable reverse processes through Markov chain modeling. However, due to the inherent randomness of probabilistic models, images generated from the sampling space by diffusion models diverge from real images. Nonetheless, these efforts typically excel only within data distributions identical to their training sets, displaying limited generalizability.Uncertainty in Deep Learning. As deep learning continues to evolve, neural networks have permeated nearly every scientific domain, becoming integral to a myriad of real-world applications [10, 17, 20, 29, 57] . Researchers have devoted significant effort to understanding and quantifying the uncertainty in neural network predictions to enhance the performance and robustness of deep networks [35, 55, 58, 62] . In the field of computer vision, uncertainty modeling plays a pivotal role in critical tasks such as image classification [40, 46] , object detection [16] , semantic segmentation [4] , face recognition [65, 67] , action recognition [41, 62] , and image generation [39] . The uncertainty in deep learning can broadly be categorized into two types: data uncertainty (also known as aleatoric uncertainty), which describes the intrinsic noise within the data, and model uncertainty (also known as epistemic uncertainty), which reflects the uncertainty inherent in the model itself due to inadequate training, insufficient training data, and other factors.As shown on Equation ( 2) , given a parameterized model 𝑓 (𝜃 ), model uncertainty is formalized as a probability distribution over the model parameters 𝜃 , while data uncertainty is formatted as a probability distribution over the model output 𝑦 * . The term 𝑝 (𝜃 |𝐷) is referred to as the posterior distribution of model parameters. 𝐷 indicates the training dataset [11] . Our work focuses on model uncertainty within image degradation representation.EQUATIONUnvertainty-based SR. To date, only a limited number of studies have explored the potential of uncertainty modeling in the task of super-resolution. SOSR [2] investigated the issue of source-free domain adaptation within super-resolution tasks through uncertainty modeling. DDL [30] utilized Bayesian methods to assess the reliability of high-frequency inference from a frequency domain perspective. [36] introduced an uncertainty-driven loss function that incorporates per-pixel uncertainty into super-resolution, giving priority to pixels with greater certainty, such as those representing texture and edges. Another study employed batch normalization uncertainty to analyze super-resolution uncertainty, thereby enhancing network robustness against adversarial attacks [22] . GRAM [25] focused neural network attention on challenging images through Gradient Rescaling Attention. However, none of these efforts addressed the blind super-resolution challenge associated with complex degradation processes. 
method 3.1 overview: 
 As previously mentioned, implicit modeling of complex and variable degradation processes represents a promising approach to addressing the issue of image blind super-resolution, with the lack of ground truth posing a significant challenge. To tackle this challenge, we employs an Uncertainty-based Degradation Representation (UDR) to model various degradation processes, adapting to complex and varied degradation scenarios. As illustrated in Figure 2 (b), we implement Adaptive Uncertainty-Aware Degradation Extraction (AUDE) on LR images. In detail, we obtain the UDR from LR images using the Degradation Extractor (DE). To facilitate self-supervised training of UDR, we have devised the USLoss and an Self-supervised Uncertainty Contrast module. As depicted in Figure 2 (c) , to effectively harness the information encapsulated within the UDR, we have designed a Variable Depth Dynamic Convolution (VDDC) Block, which facilitates the modulation of UDR intensity in accordance with the depth of the network.Overall, we utilize the DE to derive the UDR from LR images. Concurrently, the LR image undergoes initial shallow feature extraction via a convolutional layer, followed by deep feature extraction through 𝑁 VDDCs.Within AUDE, DE is tasked with extracting UDR from LR images. To suppress the uncertainty in the DE network, we employed USLoss within the Self-supervised Uncertainty Contrast to conduct selfsupervised training of DE. Degradation Extractor. Acknowledging the limitations of blur kernel modeling, which is confined to addressing solely blur-related degradation processes, we have adopted a more potent approach of implicit modeling for a comprehensive characterization of the degradation process. As depicted in Figure 2 (b), the DE extract degradation representations from LR images adaptively. Initially, DE subjects the image to a preliminary feature extraction phase involving a 3 × 3 convolutional layer followed by a ReLU layer. This is succeeded by the application of multiple convolutional blocks tasked with the extraction of deeper features. Each convolutional block is systematically composed of a 3 × 3 convolutional layer, a ReLU layer, and another 3 × 3 convolutional layer, arranged in sequence. The final stage of this process involves the refinement of the degradation representation vector through an average pooling layer and a Multilayer Perceptron (MLP).Within the process of AUDE, during the training phase, two distinct patches are obtained from a LR image. The DE is employed to extract the corresponding UDR from these patches. By applying USLoss to suppress the uncertainty associated with these two UDRs, we guide the DE towards more stable degradation estimations. During inference, DE directly extracts a global UDR from the entire LR image. Self-supervised Uncertainty Contrast. While different parts of the same image undergo nearly identical degradation, as illustrated in Figure 2 (b), the DE estimates significantly varied degradation representations from different patches of a LR image. This discrepancy reveals that without meticulous constraints, the UDR derived by DE is inconsistent and unstable, thus incapable of furnishing precise degradation information for subsequent SR networks. Ideally, however, the UDR obtained from different patches or the entire image should exhibit consistency. Our objective is for DE to estimate a unified and accurate degradation representation, both locally and globally. To address this challenge, we have designed a USLoss and an Self-supervised Uncertainty Contrast module to mitigate this uncertainty, enabling DE to estimate degradation representations more stably and accurately.Let 𝑢 1 and 𝑢 2 represent the degradation representations obtained from two different patches 𝑥 1 and 𝑥 2 within a LR image. From probabilistic perspective, our training objective aims to maximize the joint distribution probability in Equation (3): where 𝑊 represents the parameters of the model. To maximize the aforementioned objective, we should aim to minimize the negative log likelihood of the joint distribution probability, where the random variables are 𝑢 1 and 𝑢 2 . Therefore, the optimization objective transforms into Equation (4):EQUATIONEQUATIONTo address this probability distribution, we model 𝑃 (𝑢 |𝑥;𝑊 ) as a multivariate normal distribution, drawing upon the Central Limit Theorem (CLT) [24] and non-local means [7] . When there are sufficiently many random variables, their sum or average tends toward a normal distribution. This leads to Equation (5):EQUATIONThe mean 𝑁 and covariance Σ, outputs of the network parameterized by 𝑊 , form the crux of our approach. However, the expectation in Equation ( 4) necessitates sampling 𝑢 from 𝑃 (𝑢 1 , 𝑢 2 ), an operation intrinsically non-differentiable. To facilitate backpropagation through 𝑢, we employ reparameterization [23] to transfer the sampling process to the stochastic variable 𝑧 ∼ 𝑁 (0, 1), culminating in Equation ( 6):EQUATIONIn accordance with the conditional model delineated by the Gibbs distribution [6, 12] , we arrive at Equation (7) :EQUATIONThe corresponding Gibbs energy is expressed in the form |𝑢 1 -𝑢 2 |, with 𝑘𝑇 denoting the constant partition function [14] . Hence, we derive Equation ( 8):EQUATIONEquation ( 8) impartially treats patches from different regions of an image. However, due to the inherent model uncertainty present in the feature extraction process of DE, the degradation representations extracted from different patches are not stable [10, 11, 48] . Aiming to direct DE towards a representation of degradation with diminished uncertainty, we have conceptualized the Self-supervised Uncertainty Contrast module. Within this module, a sequence of a linear layer and a Sigmoid layer is employed to deduce a learnable variable, the Uncertainty Aware weight 𝛼. This weight is then multiplied by the degradation representation to adaptively modulate its expressive intensity. Furthermore, we refine Equation (8) , orienting it towards a suppression of the uncertainty estimated by DE in the direction of lower uncertainty, yielding Equation ( 9):EQUATIONTo prevent the DE from degrading to a local optimum during the training process, we incorporate a regularization term based on the Uncertainty Aware weight 𝛼. This compels the Self-supervised Uncertainty Contrast module to discriminate the uncertainty among different patches, as indicated in Equation ( 10):EQUATIONIn summary, the final USLoss is encapsulated in Equation ( 11), where 𝜆 represents scaling weight.EQUATIONThrough Equation ( 11), we have mitigated the uncertainty inherent in the DE estimation process of degradation representation, which we term as Uncertainty-based Degradation Representation (UDR).Through uncertainty modeling, we acquire the UDR via the Degradation Extractor (DE). To leverage UDR to its fullest extent, we have crafted the Variable Depth Dynamic Convolution (VDDC) Block, which adjusts the intensity of UDR based on the depth of the network and efficiently mines the feature information of images.In the feature extraction segment, we adopt the design from HAB [8] , wherein the channel attention within HAB and the design of the window-based multi-head self-attention [31] have been proven to effectively extract features. Each VDDC, in addition to 𝑡 HABs, also includes a residual group composed of a layer normalization, a W-MSA layer [31] , another layer normalization and an MLP, as well as a 3 × 3 convolutional layer for fine-tuning the features.As illustrated in Figure 2 (c) and inspired by KDSR [53] and UDVD [54] , we integrate UDR using dynamic convolution. We first reshape UDR to the dimensions of 𝑐 × ℎ × 𝑤, and let 𝐹 denote a feature map with dimensions 𝐶 × 𝐻 × 𝑊 . For each channel 𝑖, the convolution output 𝑂 𝑖 at position (𝑥, 𝑦) can be described by Equation (12) .EQUATIONwhere 𝑢 represents the dynamic convolution weights, and 𝑂 denotes the output features with dimensions 𝐶 × 𝐻 × 𝑊 . However, in such a feature fusion approach, the UDR introduced at different depths of the network remains constant. A more rational approach would involve adaptively adjusting the intensity of the UDR input based on the network hierarchy. Thus, inspired by the concept of [46] , we perform an Adaptive Intensity Scaling operation (AIS) on UDR before the dynamic convolution, as illustrated in Equation (13) .EQUATIONThe adaptive scaling parameter 𝛾 is derived as Equation (14) .EQUATION)where 𝜎 refers to activation function, 𝑊 represents the transformation matrix and 𝑏 represents the linear bias. 
experiment 4.1 experiment setup: 
 Implementation details. In the DE, the number of convolutional blocks is set to 5; the MLP consists of three linear layers and three LeakyReLU layers in alternation. The scaling weight 𝜆 in USLoss is set to 0.1. USR incorporates 7 VDDC blocks, each containing 6 HABs.For the activation function in Equation ( 14), we have chosen the Sigmoid. The MLP within the VDDC adheres to the configuration specified in [31] . The final Reconstruction segment comprises a convolutional layer, a pixel shuffle layer, and another convolutional layer to upsample the feature map into an image. USR is trained on a mixed dataset comprising DIV2K and Flickr2K. The training process is divided into three stages: (1) We train the VDDC to extract image features using MSE Loss; (2) Subsequently, by leveraging USLoss to suppress model uncertainty, we undertake self-supervised training of the DE; (3) Finally, the network is finetuned using L1 Loss. More Implementation details will be offered in the supplementary materials. Testing datasets. We conducted comparisons between USR and several representative methods across six widely used datasets: DIV2K [1] , BSDS [3] , Urban100 [18] , T91 [56] , DPED [19] and DRealSR [51] . Synthetic data were generated following the workflow proposed by Real-ESRGAN [48] .Compared Methods. To evaluate the effectiveness and performance of our method, we compared USR with current state-of-theart and representative blind super-resolution approaches, including DAN [34] , DCLS [33] , DASR [47] , MANet [27] , KDSR [53] , SwinIR [26] , HAT [8] , Real-ESRGAN [48] , and ResShift [59] . We tested these methods using their officially available codes. Quantitative Comparisons. Quantitative results from the Table 1 reveal USR's significant advantages across multiple datasets, particularly in the image super-resolution domain. At a ×4 magnification factor, USR achieved the highest PSNR and SSIM on nearly all datasets.On the DIV2K validation set, USR reached a PSNR of 23.96 dB, surpassing other methods, and led in SSIM with a score of 0.78, demonstrating its exceptional capability in restoring high-quality images. Similarly, on the BSDS100 and Urban100 datasets, USR not only led in PSNR with scores of 29.89 dB and 22.53 dB, respectively, but also achieved the highest SSIM scores of 0.92 and 0.77, further proving its robust performance across different types of images.Notably, on the DRealSR dataset, even when facing complex realworld scenes, USR maintained a high level of performance with a PSNR of 31.02 dB, slightly higher than other methods. This result is particularly significant considering the test's closer alignment with Overall, USR's consistently high performance across various datasets highlights its remarkable advantages in the field of image super-resolution, especially in handling real-world images. Comparing different methods shows that USR excels not only in traditional evaluation standards but also in adapting to and managing complex real-world scenes. Qualitative Comparisons. As illustrated in Figure 3 , due to the inherent challenges of the blind super-resolution task, super-resolution models encounter issues such as distortion and artifacts. Compared to other methods, USR excels in preserving the authenticity of the original image and restoring details. Observing the HR image alongside the image processed by USR, one can clearly see USR's significant advantage in maintaining the overall structure and color fidelity of the image.In the case of stained glass, USR not only faithfully preserves the color and sheen of the glass but also demonstrates superior clarity in edges and details compared to other methods. Particularly in the intricate depiction of the stained glass's central pattern, USR reveals detail levels and color gradients close to the original, whereas other methods exhibit some distortion in these aspects.In urban street scene case, USR similarly showcases its strengths. Observing the window frames and the brickwork on walls, USR's precision in detail restoration is evident. In contrast, other methods are either too blurry, losing some details, or too sharp, resulting in unnatural artifacts along edges. USR achieves a good balance in handling these details, restoring true textures and depth, making the image closer to the original high-resolution version.In summary, USR not only provides a more natural and smooth overall visual experience but also maintains a high fidelity in restoring everything from subtle textures to macro structures. Its performance surpasses other super-resolution methods in several aspects, whether it's in detail sharpening, color accuracy, or the naturalness in avoiding over-processing. More Qualitative comparision results will be offered in the supplementary materials.Effectiveness of AUDE and AIS. As shown in Table 2 , we conducted experiments across multiple datasets to validate the effectiveness of AUDE and AIS. AUDE is a crucial component of USR, achieving the implicit representation of the image degradation process; AIS is a vital element of VDDC, performing adaptive intensity adjustments to UDR based on the network hierarchy.Evaluation results on three distinct datasets-DIV2K, BSDS100, and Urban100-demonstrate that the simultaneous application of AUDE and AIS yields the highest PSNR and SSIM scores. Notably, on the DIV2K dataset, PSNR and SSIM reached 23.96 and 0.78, respectively; on the BSDS100 dataset, scores were 29.89 and 0.92, respectively; and on the Urban100 dataset, the scores were 22.53 and 0.77. This starkly contrasts with results obtained using AUDE or AIS alone, where the performance with just AUDE outperforms that with only AIS.As shown in Table 3 , we also analyzed the performance with different numbers of VDDC blocks 𝑁 . The findings reveal that an optimal performance is achieved with 7 VDDC blocks (our approach), manifesting in a PSNR of 23.96 and a SSIM of 0.78 on DIV2K; a PSNR of 29.89 and a SSIM of 0.91 on BSDS100; and a PSNR of 22.53  
: 
 We randomly select different patches from the same image to compare the mean and variance of the degradation representations obtained by DASR, KDSR and USR. These methods exhibit varying degrees of instability, rooted in the inherent uncertainties of the model. USR (ours) demonstrates the most stable performance among them. by augmenting the pixel count. This process involves deducing and restoring high-frequency details from a limited array of pixel information, thereby yielding images of enhanced clarity and detail. Conversely, image degradation represents the reverse procedure, wherein LR images are generated from their HR analogs. The degradation process is often unknown and complex, rendering the issue of blind super-resolution a formidable challenge. Modeling the image degradation process aids in reducing the complexity encountered by image SR models.Traditional super-resolution techniques typically rely on interpolation methods [15, 43] . However, with the advent of deep learning, methods based on neural networks have significantly outpaced traditional approaches. These methods fall into two categories: model-based methods and learning-based methods. Model-based approaches simulate the image degradation process, estimating the degradation mode of LR images before reconstructing the HR images. These methods range from simple to complex, including those based on blur kernel estimation [13] , spatially variant blur kernels [27, 66] , and implicit modeling [21, 32, 47, 53] of the degradation process. They estimate the degradation mode for each LR image individually, hence are inclined to better generalize across unknown degradations. On the other hand, learning-based methods aim to train a unified super-resolution network using a vast corpus of LR/HR image pairs synthesized based on presumed degradation models [37, 64, 68 ]. Yet, these learning-based approaches are heavily dependent on the training data and may suffer significant performance drops when there is a domain discrepancy between the training and testing data [49] . Some efforts attempt to simulate real-world degradation patterns with more complex training samples, notable among which are BSRGAN [63] and Real-ESRGAN [48] , employing advanced degradation models that incorporate blur, noise, resizing, and JPEG compression to generate training samples.Researchers widely regard the challenge of addressing such complex degradation processes as blind super-resolution [42, 52, 53] .Model-based approaches predominantly rely on blur kernels. However, these methods possess limited representational capacity and can only cover blur-related degradation, falling short in the face of noise, JPEG compression, and other complex degradation processes. To address the nearly infinite degradation modes in blind super-resolution tasks, recent works have proposed using implicit modeling to characterize degradation patterns. DASR [47] employs contrastive learning to distance or draw closer feature representations of different degradation modes, whereas KDSR [53] uses knowledge distillation to enable a student network to learn the degradation representation from a teacher network.As illustrated in Figure 1 , DASR and KDSR exhibit instability in estimating degradation representations, meaning they fail to obtain consistent degradation representations for the same LR image. Such instability and inaccuracies in degradation representation adversely affect subsequent super-resolution processes. This instability is a manifestation of model uncertainty [11] . The root causes of the instability and unreliability in the degradation features of these methods are: (1) Due to the absence of ground truth, these methods provide only coarse constraints on the estimation process. (2) They overlook the uncertainty present in estimating implicit degradation representations, failing to offer confidence or uncertainty estimates for the generated outcomes.To address this issue, we introduce an Uncertainty-based degradation representation for blind Super-Resolution (USR) framework. To quantify and mitigate the uncertainty in Uncertainty-based Degradation Representation (UDR) estimation, we constrain UDR with Self-supervised Uncertainty Contrast which suppress the uncertainty of local degradation representations in images. Furthermore, to ensure effective guidance of UDR, we have designed Variable Depth Dynamic Convolution (VDDC) Block. Thorough experimentation validates the efficacy of our proposed modules. In summary, our contributions are as follows:  
 
 Appendix:  


Decision: The paper received mixed reviews (2x Borderline Accept, 1x Weak Reject) and the authors provided a response to the raised concerns.

Reviewer 8Stg (Weak Reject) appreciates that the authors addressed most of his concerns, however, still has concerns with respect to reported results (Table 1, Fig.3) and demands reproducibility codes and explanation. 

The other reviewers recommend acceptance appreciating the contributions and that "the performance is charming".

After carefully reading the paper, the reviews, the authors' response and the reviewers' final justifications, the Meta-Reviewer agrees that the paper indeed makes significant contributions to a challenging problem -- blind super-resolution, and achieves promising results. At the same time, in Fig.3 the visual result is indeed questionable and the authors should make sense out of it and the provided authors' response contains important information that should be in the paper.

The paper is worthy for publication and the authors are invited to refine their work based on the received feedback and integrate relevant contents from their response to the camera ready paper.

Also, the authors are encouraged to release codes for reproducibility.

Reviews: review: paper_summary: paper_summary: This work introduces an uncertainty-based degradation representation for blind super-resolution framework. To quantify and mitigate the uncertainty in uncertainty-based degradation representation estimation, the work proposes the self-supervised uncertainty contrast to suppress the uncertainty of local degradation representations in images. Extensive experiments demonstrate the effectiveness of the proposed method.
strength_weakness: strength_weakness: - It proposes variable depth dynamic convolution blocks and adaptive uncertainty-aware degradation extraction in the SR process.
- Multiple datasets have been validated.
- The PSNR/SSIM is promising.
limitations: limitations: - The motivation of the work is not presented clearly. Why we should suppress the uncertainty and how to achieve it are not shaped well.
- The results in Fig.3 show limited improvement of the proposed method. Even, from my view, the proposed method is not good as for the first image in the figure.
paper_summary: paper_summary: The paper proposes an \textbf{U}ncertainty-based degradation representation for blind \textbf{S}uper-\textbf{R}esolution framework (\textbf{USR}). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks.
strength_weakness: strength_weakness: The topic is of interesting.
limitations: limitations: 1)	In equation (3), for the joint probability is seen as independent of each other, but for x1 and x2, taken from different regions of the same image with the same degradation pattern, there will be mutual influence. Why it can be seen to be independent of each other?
2)	The USR method performs well on other datasets but is more than 2 db lower than DCLS on T91, which is also a natural image and should perform similarly to datasets such as Urban100. What is the main reason ? Please explain it.
3)	In equation (5), the mean and variance are output from the network parametered by W. How should this be defined, and how does one go about outputting the mean and variance?
4)	How does Uncertainty Aware weight reduce the uncertainty of the degenerate representation in Eq. (9), and why is it a subtraction of the canonical term for Eq. (11)?
5)	Is the dataset generated using Real-ESRGAN during the training of the network, and how about the number of parameters for different algorithms?
6)	In the ablation experiment in Table2, several cases are missing. The effect of the model not used by either AUDE or AIS should be given. What is the case in Table 3, where the number of VDDC is equal to 9?
paper_summary: paper_summary: For the Blind Image Super-Resolution task, this work proposes the Uncertainty-based Super-Resolution (USR) framework by using implicit modeling, which includes two core modules: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and Self-Supervised Uncertainty Contrast module. The former aims at extracting degradation representations, while the latter aims at adding constraints to degradation representations via uncertainty learning. Finally, the accurate degradation representation is used to guide the network reconstruction.
strength_weakness: strength_weakness: The paper is well-organized, ensuring it is easily understandable.
limitations: limitations: (1) Figure 2 is not easy to understand. My understanding is as follows:

- For (a), the input LR extracts initial features through Conv, and then the initial features and UDR are fed into the cascaded VDDC for reconstruction. Then the line from LR to Conv should not be placed under AUDE, which is easy to confuse. Finally, is the UDR fed into the VDDC during training based on the global image?
- For (b), the expression in the figure means that a and u are multiplied point-wise and then the UDR is obtained by USLoss. Is there any - ambiguity with the expression in the article? My understanding from reading the article is that DE gets the degradation representation UDR after the MLP layer. To mitigate the uncertainty inherent in the degradation representation in the DE estimation process, two image patches are randomly selected for self-supervised uncertainty comparison learning. Finally, the UDR is fed to the VDDC after adaptive adjustment via Equation 13. So, in equation 13, u represents the dynamic convolutional weights, i.e. the degradation representation UDR?
- For (c), where are the features of the input HAB? Is the result of the dot product of the image features and UDR added at the end of the module? Also, u already represents the degradation mapping of the image patch (L341), it is better not to use u to represent the dynamic convolutional weights (L514). If both represent the same thing, it should be specified in L514.

(2) I have a small question about the motivation for this paper:
- The following is from L327-339 in the paper：
“While different parts of the same image undergo nearly identical degradation.”
“Ideally, however, the UDR obtained from different patches or the entire image should exhibit consistency.”
- However, degradation in real-world images is often not similar in different local regions. This is because image degradation is affected by a variety of factors, and the way these factors are distributed and act in an image may vary from region to region.
For example.
non-uniform motion blur produces different blurring effects in different regions.
Noise is not uniform in distribution and intensity in an image, similarly, noise characteristics may vary from region to region.
Compression may vary from region to region depending on the compression rate and image content.

(3) Non-reference metrics can be considered to add for convincing enhancement, similar to SeeSR (CVPR2024), given that some of the methods in Table 1 are GAN-based, it is not fair to compare PSNR/SSIM.

(4) In Table 1, for the datasets div2k, bsds100, urban100, t91, they are first synthesizing low-resolution images via degradation process of RealESRGAN and then used for testing? dped and realSR are tested directly using the low-resolution images?

(5) The references are confusing and for the conference reference, four different versions appear:
- In ICCV.
- In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
- In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16. Springer, 272–289.

(6) Some arXiv references can be replaced with published versions.