UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation

Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 13:13:19

Abstract: Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restoration methods, they overlook the utilization of commonalities and specificities within multi-task restoration, thereby impeding the model's performance. Inspired by the success of deep generative models and fine-tuning techniques, we proposed a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks using low-rank adaptation. Additionally, we introduce a LoRA composing strategy based on the degradation similarity, which adaptively combines trained LoRAs and enables our model to be applicable for mixed degradation restoration. Extensive experiments on multiple and mixed degradations demonstrate that the proposed universal image restoration method not only achieves higher fidelity and perceptual image quality but also has better generalization ability than other unified image restoration models.

Main paper: 
 introduction: 
 In the wild, a range of distortions commonly appear in captured images, including noise [56] , blur [14, 47, 6] , low light [58, 22, 8] , and various weather degradations [15, 51, 54, 45] . As a fundamental task in low-level vision, image restoration aims to eliminate these distortions and recover sharp details and original scene information from corrupted images. With the assistance of deep learning, an abundance of restoration approaches [56, 3, 54, 2, 16, 14, 53] have made significant progress in eliminating single degradation from images. However, these approaches typically require additional training from scratch on specific image pairs in multi-degraded scenarios, which leads to inconvenience in usage and limited generalization ability.For simplicity and practicality, some existing works [15, 31, 55] consider training a unified model (also called all-in-one model) to handle multiple degradations as multi-task learning. These studies primarily explore how to discern degradation from the image and integrate it into the restoration network. Nevertheless, these methods share all parameters across different degradations, resulting in gradient conflicts [40, 52] that hinder further improvement of unified models' performance.Digging deeper, the underlying issue lies in that the similarities among different image restoration tasks and the inherent specificity of each degradation are not well considered and utilized in the training. This limitation drives us to seek solutions for multi-degradation restoration by leveraging both commonalities and specificities.Submitted to 38th Conference on Neural Information Processing Systems (NeurIPS 2024). Do not distribute. Inspired by the successes of deep generative models [37, 36, 35] and fine-tuning techniques [11, 10, 4] ,we propose addressing the aforementioned issue from the perspective of multi-domain transfer learning, as presented in Figure 1 . The pre-trained generative model exhibits powerful capabilities, implying rich prior knowledge of clear image distribution p(x), which is exactly what is needed for image restoration. Since image prior p(x) is degradation-agnostic and applicable to all types of degraded images, the pre-trained generative model is an excellent candidate for serving as the shared component for multiple degradation restoration. To model the transition from the clean image domain to different degraded image domains, minimal specific parameters are required to fine-tune the pre-trained model for each degradation restoration task. This approach not only isolates conflicts between each degradation task but also ensures efficiency and performance during training.Following the idea of multi-domain transfer learning, we proposed a universal image restoration framework based on multiple low-rank adaptations, named UIR-LoRA. In our framework, the pretrained SD-turbo [39] serves as the shared fundamental model for multiple degradation restoration tasks due to its powerful one-step generation capability and extensive image priors. Subsequently, we incorporate the low-rank adaptation (LoRA) technique [11] to fine-tune the base model for each specific image restoration task. This involves augmenting low-dimensional parameter matrices on selected layers within the base model, ensuring efficient fine-tuning while maintaining independence between LoRAs for each specific degradation. Additionally, we propose a LoRA composition strategy based on degradation similarity. We calculate the similarity between degradation features extracted from degraded images and existing degradation types, utilizing it as weights for combining different LoRA experts. This strategy enables our method to be applicable for restoring mixed degradation images. Moreover, we conducted extensive experiments and compared our approach with several existing unified image restoration methods. The experimental results demonstrate that our method achieves superior performance in the restoration of various degradations and mixed degradations. Not only does our approach outperform existing methods in terms of distortion and perceptual metrics, but it also exhibits significant improvements in visual quality.Our contributions can be summarized as follows:• From the perspective of multi-domain transfer learning, we propose a novel universal image restoration framework based on multiple low-rank adaptations. It leverages the pre-trained generative model as the shared component for multi-degradation restoration and employs distinct LoRAs for multiple degradations to efficiently transfer to specific degradation restoration tasks.• We introduce a LoRAs composition strategy based on the degradation similarity, which adaptively combines trained LoRAs and enables our model to be applicable for mixed degradation restoration.• Through extensive experiments on multiple and mixed degradations, we demonstrate that the proposed universal image restoration method not only achieves higher fidelity and perceptual image quality but also has better generalization ability than other unified models. [2, 53, 16, 56, 5, 14] mainly address the issue with a single degradation. Traditional approaches [27, 28, 7] have proposed image priors. While these priors can be applied to different degraded images, their capability is limited. Due to the remarkable capability of the deep neural network (DNN), numerous DNN-based methods [2, 53, 16] have been proposed to tackle image restoration tasks. While DNN-based methods have made significant progress, they struggle with multiple degradations and mixed degradations, since they typically require retraining from scratch on data with the same degradation.Universal degradation restoration. Increasing attention is currently focused on developing a unified model to process multiple degradations. For example, AirNet [15] explores the degradation representation in latent space for separating them in the restoration network. PromptIR [31] utilizes a prompt block to extract the degradation-related features to improve the performance. Daclip-IR [20] introduces the clip-based encoder to distinguish the type of degradation and extract the semantics information from distorted images and embed them into a diffusion model to generate high-quality images. Despite the advancements, these unified models still have limitations. They also require retraining all parameters when unseen degradations arrive and have limited performance due to the gradient conflict. 
low-rank adaptation: 
 LoRA [11] is proposed to fine-tune large models by freezing the pre-trained weights and introducing trainable low-rank matrices. This fine-tuning method leverages the property of "intrinsic dimension" within neural networks, lowering the rank of additional matrices and making the re-training process efficient. Concretely, given a weight matrices W ∈ R n×m in pre-trained model θ p , two trainable matrices B ∈ R n×r and A ∈ R r×m are inserted into the layer to represent the LoRA ∆W = BA, where r is the rank and satisfy r ≪ mim(n, m), the updated weights W ′ are calculated byEQUATIONBy applying LoRA in pre-trained models, numerous image generation methods [29, 13] , show superior performance in the field of image style and semantics concept transferring. Additionally, fine-tuning methods like ControlNet [57] , T2i-adapter [24] are also commonly employed in largescale pre-trained generative models such as Stable Diffusion [37] , SDXL [30] , and Imagen [38] .Mixture of Experts (MoE) [41, 49, 48] EQUATIONWe will present how to design and optimize the trainable {θ k } T k=1 and construct the composite weights s in the next sections.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: The paper does discuss the limitations of the work in Sections 6.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.• The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.• The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.• The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.• The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.• If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.• While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
overview of universal framework: 
 Inspired by transferring learning, we introduce a novel universal image restoration framework based on multiple low-rank adaptations, named UIR-LoRA. Referring to Figure 2 , our framework consists of two main components, namely degradation-aware router and universal image restorer, respectively.The degradation-aware router first extracts the degradation feature from input degraded images and then calculates the similarity probabilities s with existing degradations in the latent space of CLIP model [35, 20] . For the universal image restorer, it comprises a pre-trained generative model θ p and T trainable LoRAs {θ k } T k=1 . This design is primarily motivated by two considerations: firstly, the pre-trained generative model contains extensive image priors that are degradation-agnostic and can be shared across all types of degraded images. Secondly, each LoRA can independently capture specific characteristics of each degradation without gradient conflicts. In practice, the pre-trained SD-turbo [39] is employed as the frozen base model in our framework and each LoRA θ k serves as an expert responsible for transferring the frozen base model to a specific degradation restoration task D k . By adjusting the value of Top-K parameter within the degradation-aware router, different combinations of LoRAs in the universal image restorer can be activated, enabling the removal of a specific degradation and mixed degradation in multi-degraded scenarios.The Degradation-Aware Router is designed to provide the restorer with weights for LoRA combination based on degradation confidence. Following Daclip-ir [20] , we utilized the pre-trained image encoder in CLIP [35] to obtain the degradation vector d ∈ R 1×z from the input degraded image x, where z is degradation length in latent space. Differing from Daclip-ir [20] , we use the degradation vector and existing degradations to calculate the similarity, instead of directly embedding the degradation vector into the restoration network in Daclip-ir [20] . The existing degradations refer to the vocabulary bank of diverse degradation types that we introduce in the router, such as "noisy", "blurry" and "shadowed".This vocabulary bank is highly compact and flexible when adding new degradation types. Similarly, by applying the text encoder of CLIP [35] , the vocabulary bank can be encoded into the degradation bank B ∈ R z×T in the latent space. As presented in Figure 2 , the original degradation similarityEQUATIONBuilding upon the original similarity, we adopt a more flexible and controllable Top-K strategy to modify s o . Specifically, we select the Top-K largest values from the original similarity s o , and normalize them to reallocate the weights for LoRAs. The reallocation process can be formulated as :EQUATIONwhere M K represents a binary mask with the same length as s o , where it is 1 when the corresponding value in s o is among the Top-K, otherwise it is 0. With a smaller value of K, the restorer activates fewer LoRAs, reducing its computational load. For instance, with K = 1, only the most similarLoRA is activated and it yields effective results when s is accurate, but performance noticeably declines with inaccurate s. Conversely, as K increases, the restorer exhibits higher tolerance to s and the combination of LoRAs allows it to handle mixed degradation.Our universal image restorer consists of a pre-trained generative model θ p and a set of LoRAs aggregate the outputs of each expert rather than directly merging parameters in [11] . Therefore, given the input feature x in of the current layer and the similarity s, the total output x out of this modified layer can be expressed as{θ k } T k=1 .EQUATIONwhere f i (x in ) denotes the result of i-th trainable matrice W i , particularly f o (x in ) is output of the frozen base layer. From the equation 5, it can be observed that the introduced LoRAs interact with the frozen base model at intermediate feature layers in our restorer. This interaction forces the restorer to leverage the image priors of the pre-trained generative model and eliminate degradation with the assistance of LoRAs. In contrast to employing stable diffusion [37] directly as a post-processing technique, our restorer yields results closer to the true scene without introducing inaccurate structural details. Since each W is implemented using two low-rank matrices like the formula 1, the total trainable parameters of our framework are much smaller than that of the pre-train generative model.During the training phase, for the efficient training of the universal image restorer, we ensure that each batch is sampled from the same degradation type D k , and activate the corresponding LoRA θ k [35] and Daclip-ir [20] in our router, the router was not utilized during training.In the inference phase, the similarity s is unknown and needs to be estimated from the degraded image. The estimated similarity s serves as a reference in our framework and can also be manually specified by users. Subsequently, our universal image restorer composite LoRAs and recovers the input image with the guidance of s.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [Yes] Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [No] Justification: Upon acceptance of the paper, we will release the code under an open-source license, which will allow the community to access and verify the experimental results.Guidelines:• The answer NA means that paper does not include experiments requiring code.• Please see the NeurIPS code and data submission guidelines (https://nips.cc/ public/guides/CodeSubmissionPolicy) for more details.• While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. 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experimental setting: 
 Datasets. We validate the effectiveness of our framework in multiple and mixed degradation scenarios.In the case of multiple degradations, we follow Daclip-IR [20] and construct a dataset using 10 different single degradation datasets. Briefly, the composite dataset comprises a total of 52800 image pairs for training and 2490 image pairs for testing. The degradation types included are commonly encountered in image restoration, such as blur, noise, shadow, JPEG compression, and weather degradations. For mixed degradations, we utilize two degradation datasets, REDS [25] and LOLBlur [58] . In REDS, the images are distorted by JPEG compression and blur, and those images in LOLBlur have blur and low light. For more details about datasets in our experiments, please refer to Appendix.Metrics. The objective of the image restoration task is to output images with enhanced visual quality while maintaining high fidelity to the original scene information. This differs from image generation tasks, which prioritize visual quality. Therefore, to thoroughly evaluate the effectiveness of our method, we utilize reference-based image quality assessment techniques from both distortion and perceptual perspectives, including PSNR, SSIM, and LPIPS, as well as FID.Comparison Methods. In the experiments, we primarily compare with several state-of-the-art methods in image restoration, which fall into two categories: regression model and generative model.Regression models include NAFNet [2] , Restormer [53] , as well as AirNet [15] and PromptIR [31] proposed for multiple degradation restoration. DiffBIR [17] , IR-SDE [21] and Daclip-IR [20] are generative models built upon the diffusion model [9] .During the training, we adapt an AdamW optimizer to update the weights of trainable parameters in our model. Before training LoRA for specific degradation, we add skip-connections in the VAE of SD-turbo [39] like [29, 44] and train them with multiple degraded images. We set the initialization The default rank of LoRAs in VAE and Unet is 4 and 8, respectively.For fair comparisons, all methods are trained and tested on the multiple degradation dataset. The results are presented in Table 1 . We can find that our model, UIR-LoRA, considerably surpasses all compared image restoration approaches across four metrics. This indicates that our approach can balance generating clear structures and details while ensuring the restored images closely resemble the original information of the scene. The visual comparison results depicted in Figure 7 also confirm this assertion. Regression models such as NAFNet [2] and Restormer [53] , lacking extensive image priors, tend to produce blurred and over-smoothed images, leading to inferior visual outcomes. Conversely, generative models Daclip-IR [20] excessively prioritize perceptual quality, yielding artifacts and noise that diverge from the actual scene information. Our approach integrates the strengths of both categories of methods, enabling strong performance in both distortion and perceptual aspectsTo evaluate the transferability of UIR-LoRA, we conduct some experiments on mixed degradation datasets from REDS [25] and LOLBlur [58] . Each image in these two datasets contains more than one type of degradation, like blur, jpeg compression, noise, and low light. We test the mixed degraded images using models trained on multiple degradations and set K to 2 in the router. As shown in Table 2 , our method achieves superior results in both distortion and perceptual quality, particularly on the LOLBlur dataset. We also provide visual comparison results, as illustrated in Figure 4 , our approach effectively enhances the low-light image compared to SOTA methods, highlighting its stronger transferability in the wild. More visual results can be found in Appendix. Complexity Analysis. We compare model complexity with SOTA models. The comparison results are shown in Table 1 , where we report the number of trainable parameters and the runtime for a 256×256 image on an A100 GPU. The complexity of UIR-LoRA is comparable to regression models like NAFNet [2] and significantly more efficient than generative models like Daclip-IR [20] .Effectiveness of Degradation-Aware Router. The degradation-aware router plays a crucial role in determining which LoRAs are activated in the inference. To comprehensively demonstrate the impact of the router, we conduct experiments with different selection strategies. As illustrated in Table 3 , we have five strategies: "random" indicates activating a LoRA at random, "average" denotes using average weights to activate all LoRAs, and "Top-1", "Top-2" and "All" correspond to setting K in the router to 1,2, and 10, respectively. From the comparison of these results, we can see that the random and average strategies result in poorer performance while using the strategy based on degradation similarity achieves better outcomes. This suggests that the transferability between different types of degradation is limited and that specific parameters are needed to address their particularities.Furthermore, the selection of the K value also affects the model's performance. When an image has only one type of degradation, a smaller K value can result in comparable performance with lower inference costs. However, for mixed degradations, a larger K value is required to handle the more complex situation.Impact of LoRA's Rank. Within our framework, LoRA is utilized to facilitate the transfer from the pre-trained generative model to the image restoration task. In order to investigate the impact of LoRA's rank on the performance of image restoration, we conduct experiments using deblurring and denoising tasks chosen from ten distinct degradation categories. We set the initial rank to 2 and incrementally increase the value by a factor of 2. The performance changes are depicted in Figure 5 .It is evident that as the rank grows, the restoration results improve in distortion and perceptual quality, and at the same time, the number of trainable parameters also increases. Once the rank value exceeds 4, the performance improvement becomes progressively marginal. Therefore, we set the default rank to 4 in our restorer to balance between performance and complexity. Impact of Predicted Degradation.The resizing operation on input images in CLIP models [20, 35] may lead to inaccurate predictions of degradation types, especially for blurry images. To reduce its negative impact on performance, we introduce a simple way that uses the degradation vector of the image crop without resizing to correct the potential error in the resized image. Table 4 is the comparison conducted on blurry images from GoPro dataset. It can be observed that our model with modified operation has higher accuracy and better performance for deblurring. 
conclusion: 
 In this paper, we propose a universal image restoration framework based on multiple low-rank adaptation, named UIR-LoRA, from the perspective of multi-domain transfer learning. UIR-LoRA utilizes a pre-trained generative model as the frozen base model and transfers its abundant image priors to different image restoration tasks using the LoRA technique. Moreover, we introduce a LoRAs' composition strategy based on the degradation similarity that allows UIR-LoRA applicable for multiple and mixed degradations in the wild. Extensive experiments on universal image restoration tasks demonstrate the effectiveness and better generalization capability of our proposed UIR-LoRA. 
limitation and discussion: 
 Although our UIR-LoRA has achieved remarkable performance in image restoration tasks under both multiple and mixed degradations, it still has limitations and problems for further exploration. For instance, adding new trainable parameters into the network for unseen degradations is unavoidable in image restoration tasks, although UIR-LoRA is already more efficient and flexible compared to other approaches. 
experiments compute resources: 
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code of ethics: 
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 Appendix: For multiple degradations, we follow Daclip-IR [20] to construct the dataset, which includes a total of ten distinct degradation types: blurry, hazy, JPEG-compression, low-light, noisy, raindrop, rainy, shadowed, snowy, and inpainting. The data sources and data splits for each degradation type are illustrated in Table 5 . For mixed degradations, we utilize images from REDS [25] and LOLBlur [58] to evaluate the transferability of models. We sample 60 images from REDS and 200 images from LOLBlur dataset for testing. The degraded images from REDS dataset feature a variety of realistic scenes and objects, which suffer from both motion blurs and compression. And the images from LOLBlur dataset cover a range of real-world dynamic dark scenarios with mixed degradation of low light and blurs.Restormer PromptIR Justification: The main claims made in the abstract and introduction1 accurately reflect the paper's contributions and scope.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper.• The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.• The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.• It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 


Decision: The final ratings for this work are as follows: 1 reject, 1 borderline reject, 1 borderline accept, and 2 weak accept, indicating a clear division in the evaluations. Specifically, reviewers nZDS and SkW8 both gave weak accept ratings. Reviewer 4LsM engaged in a thorough discussion with the authors and raised their score to marginal accept, but still expressed concerns about the contribution to performance improvement—a concern shared by Reviewer Siin. Reviewer Siin also had multiple exchanges with the authors and, while his concerns about performance comparisons were somewhat alleviated, he still had reservations about some aspects of the performance improvements. Reviewer xL9P focused more on the technical aspects of the methodology and the detailed explanations of experimental results, ultimately giving a reject rating. Overall, the ratings for this paper are significantly divided. Despite the authors addressing some concerns through in-depth discussions with the reviewers, certain critical issues remain unresolved. Therefore, the paper might still fall short of the NeurIPS 2024 standards, and I am inclined to reject it.

Reviews: review: paper_summary: paper_summary: This paper presents a framework to improve image restoration across various degradation types using Low-Rank Adapters (LoRA). The proposed method adapts a pre-trained generative model to each degradation type. It performs a weighted sum of the output of adapted models using the estimated degradation of input images. The proposed method performs impressive results in restoration accuracy and resources.
strength_weakness: strength_weakness: In Table 3, the 'Top-1' strategy performs almost the same as the 'All' strategy, which limits the motivation of the weighted sum of the adapted models.
Table 6 presents the restoration performance comparisons for each degradation. The proposed method underperforms previous works in significant degradation types such as blurry, low-light, raindrop, and rainy.
The average scores might mislead the evaluation performances.
questions: questions: How about comparing the proposed method with the following paper?
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model, CVPR 2024
limitations: limitations: The proposed method is simple and effective, but evaluating average scores on multiple degradations can mislead its contribution.
The proposed method achieves near-best performance by selecting a single adapted model but underperforms in many major degradation types.
paper_summary: paper_summary: The paper proposes universal image restoration framework using multiple low-rank adapters that learns task specific weights from to perform multi-domain transfer learning. the proposed method leverages the pre-trained generative model weights as the shared component and adapts it task specific low-rank adapters. At each layer in the restoration pipeline the proposed method uses the degradation similarity to combine LoRA adapters outputs, this enables the proposed to handle for mixed degradation restoration.
strength_weakness: strength_weakness: - In table of the paper authors compared proposed method against sota on REDS and LOLBlur datasets, both these datasets have mixed degradations  of blur, jpeg compression, noise, and low light. Although these comparisons performed on mixed degradations, it would be helpful to how the proposed method performs on mixed weather conditioned images (MID6), which is comparatively challenging than REDS and LOLBlur  datasets. 
MID6: Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration, CVPR, 2024.

- Can authors confirm, whether network re-trained seperately for each experiment in table-1,  and table-2 separately, i.e. table-1 and table-2 trained network weights for proposed method are different.
questions: questions: - Can the proposed network handle unknown degradation present in the input degradation image
- From the table-3, it is evident that Top-1, and top-2 are almost same performance as All, can authors coment on this, this makes wonder if the input image has only one degradation as dominating for this experiment. Can authors show this experiment on different dataset like MID6
limitations: limitations: - authors have addresed limitations
paper_summary: paper_summary: This submission proposes a transfer-learning based strategy to address challenges related to image-degradation restoration. The premise is that a pre-trained generative model can be employed as a common starting component for multiple degradation types, upon which distinct sets of trainable parameters (ie. low-rank adaptors) can be added in order to address specific-degradation restoration tasks. Mixed-degradation restoration is enabled through a top-K hyperparameter, that affords a mixture of (degradation) experts to be active. The experimental setup considers multi and mixed image restoration problems where average results are offered across image-degradation datasets and appropriate standard quantitative metrics, qualitative examples, are reported in comparison with alternative approaches.
strength_weakness: strength_weakness: * The key idea of leveraging pretrained VLM features (and specifically CLIP) for the task of image restoration from multiple degradations, pre-dates the current submission [R1]. While authors clearly go to some length to highlight their alternative CLIP-based scheme, which amounts to envoking specific (pre-existing [R2]) low-rank adaptors, the core technical contributions here can be regarded as somewhat limited.  

* The phrase 'Universal Image Restoration' may not be a sufficiently accurate (or modest) description for the proposed method. The submission collates ten different image restoration tasks which, despite vague statements in the abstract, remains a 'multi-task' not a 'universal' setup. Samples for all ten degradation tasks are shared between train and test (Sec. A.1) and individual task adaptors appear to be trained independently on task-specific datasets (L188--196). Generalisation ability to previously unseen degradations is also not considered. Suggest method description requires reworking.

* The claim that multi-task learning (MTL) frameworks, designed to handle image restoration for multiple degradations, share all parameters across different degradations (L029) is incomplete and somewhat misleading. Several existing MTL works (eg. [R3,R4]) make use of both shared and task-specific parameter subsets for multiple image restoration tasks. Indeed 'which proportion of parameters should be shared and which should be task specific' can be considered a fundamental (and long standing) MTL question. The idea of benefiting from commonalities between image restoration tasks is well understood and my concern is that this casts doubt on a core premise of the submission. 


References

R1. Controlling Vision-Language Models for Multi-Task Image Restoration. ICLR 2024.

R2. LoRA: Low-rank adaptation of large language models. ICLR 2022.

R3. All in One Bad Weather Removal using Architectural Search. CVPR 2020.

R4. Pre-Trained Image Processing Transformer. CVPR 2021.

Minor:

L076: 'draining' --> 'deraining'

L099: 'mim' --> 'min'

L238: 'aspects' --> 'aspects.'
questions: questions: > 'for mixed degradations, a larger K value is required to handle the more complex situtation' (L264). 

Can additional results be provided for alternative hyperparameter settings (eg. K=1 and K=10) in Tab.2, towards evidencing this claim?
limitations: limitations: Half of one sentence (L293) is apportioned to discussing method limitations. See above for suggestions on components that might make for valid additions here.
paper_summary: paper_summary: This paper proposes to perform universal image restoration via multiple low-rank adaptation. The key idea is to leverage a pre-trained stable diffusion model as the shared component and transfer it to specific degradations with LoRA adaptation. A degradation-aware router is further proposed to generate weights for LoRA combination based on degradation confidence. In experiments, the authors evaluated their method on multi-degradation and mixed-degradation datasets and conducted several ablation experiments on their core components.
strength_weakness: strength_weakness: - ControlNet is the most popular approach to adapting SD models to other tasks. I'm curious why the authors chose LoRA? As far as I know, LoRA is often used for large language models (with billions of parameters). It would be great to provide more detailed motivation in the introduction.
- In line 123, maybe it's better to use "concatenate" or other operators instead of "add" to present the unified parameters. Here, the weight $s_k$ can be ignored.
- Can the authors use other SD models as the base model? I believe applying LoRA to a multi-step diffusion process can further illustrate its efficiency.
- In Eq. (4), $s_0 \cdot M_k$ is used in both numerator and denominator, which seems weird and confusing.
- The mixed degradation experiment is cool. It would be interesting if the authors could apply their model to real-world degraded images.
- Line 45: proposed -> propose
questions: questions: In the degradation-aware router, have you finetuned the CLIP to align degraded images with correct degradation names? How do you choose the degradation names as the vocabulary bank?
limitations: limitations: See Weaknesses.
paper_summary: paper_summary: This paper introduces a universal image restoration framework UIR-LoRA based on multiple low-rank adapters. UIR-LoRA employs the pre-trained text-to-image diffusion model SD-turbo as the shared component. It utilizes a LoRA composing strategy based on the degradation similarity predicted by CLIP encoder to combine different LoRA modules. Experiments show the effectiveness of the proposed method.
strength_weakness: strength_weakness: 1. UIR-LoRA adopts SD-turbo as the pre-trained backbone for image restoration. However, SD-tubo utilizes VAE with high compression rate to encode input images, resulting in severe detail distortion for image restoration. This issue has been widely discussed in recent published works [1,2]. However, the paper ignores this very important issue in the Method Section and only mentions the skip-connections for VAE in Line 223.
2. The degradation-aware router seems to be unreliable. I do not believe that the original pre-trained CLIP Text Encoder can distinguish between different degradations through degraded text representations, such as "rain" and "raindrop". Therefore, DA-CLIP fine-tunes the original CLIP. But this paper doesn't contain any discussions about this.
3. This paper does not provide complete technical details, such as how the LQ image is used as a condition for SD-turbo. Is ControlNet used, or is it directly concatenated? I do not see any information about this in the paper. 
4. Tab. 1 only reports the trainable Param for UIR-LoRA. I think it's necessary to report the overall Param of the model. In addition, the reported PSNR for DiffBIR is very low. Did the authors add skip-connections to the VAE of DiffBIR for a fair comparison?
5. The visual results in Fig. 3 seem strange. The visual results of Restormer show noticeable artifacts between patches. Do the authors test Restormer using a tiled mode? As far as I know, using a single A100 GPU (Line 251), Restormer can restore the entire image without encountering out-of-memory issues.

[1] Wang, Wenjing, et al. "Zero-Reference Low-Light Enhancement via Physical Quadruple Priors." In CVPR, 2024.

[2] Geng, Zigang, et al. "Instructdiffusion: A generalist modeling interface for vision tasks." In CVPR, 2024.
questions: questions: 1. Authors should discuss the skip-connections for VAE in the Method Section with more details.
2. Can authors provide the degradation prediction accuracy for more different predictions (eg, rain/raindrop)?
3. Authors should provide more technical details of the proposed method.
4. More experimental results and explanations should be included.
limitations: limitations: Yes

Comments: title: Reminder for review
Thanks for the rebuttal
Official Comment by Reviewer 4LsM
comment: Dear Reviewer nZDS, I have noticed that you have not yet responded to the authors' rebuttal. I kindly urge you to engage in a discussion with the authors at your earliest convenience to help advance the review process.
Thanks for your response. I will consider adjusting the score after the discussion.
Thank you for your discussion. Your positive rating is meaningful to us.
Thanks for your patient response. We are willing to provide a clearer description to address this issue.

First, it is necessary to clarify that using average metrics is one of the common ways in all-in-one image restoration, like Table4 in [R4] and Table3 in [R5]. Additionally, performance evaluation needs to be conducted from multiple perspectives, including the comparison of visual results and comparisons across various metrics. We have provided detailed results for these aspects in our paper.  
About PSNR in Table6 in our paper, as we mentioned in the initial rebuttal, it is difficult to accurately assess the quality of an image using the PSNR metric alone. The reason can be found in numerous image quality assessment studies[R2, R3]. For example, [R2] believes that "improvement in reconstruction accuracy is not always accompanied by an improvement in visual quality". [R3] notices an "increasing inconsistency between quantitative results and perceptual quality". Therefore, we use multiple metrics to assess image quality in our paper, and please do not overlook the improvements of our method in SSIM, LPIPS, and FID.
Additionally, we provide a more direct and detailed comparison of our method and R1 on each type of degradation and each metric, as shown in T1. 

We hope our explanation and comparison with T1 can address this concern.

T1:
|       |  Blurry  |  Hazy   |  Jpeg   |  Low-light  |  Noisy  |  Raindrop  |  Rainy  |  Shadowed  |  Snowy  |  Inpainting  |  Average  |
| --------- | ---------- | --------- | --------- | ------------- | --------- | ------------ | --------- | ------------ | --------- | -------------- | ----------- |
|  PSNR-R1   |  26.13   |  27.66  |  **27.29**  |  19.42      |  27.69  |  30.07     |  27.31  |  26.39     |  27.38  |  18.85       |  25.82    |
|  PSNR-Ours   |  **26.66**   |  **30.28**  |  27.15  |  **22.45**      |  **27.74**  |  **30.51**     |  **28.26**  |  **28.63**     |  **28.09**  |  **30.88**       |  **28.06**    |  
|  SSIM-R1  |  0.821   |  0.937  |  **0.792**  |  0.822      |  0.771  |  **0.913**     |  0.828  |  0.850     |  0.884  |  0.769       |  0.838    |
|  SSIM-Ours   |  **0.839**   |  **0.962**  |  0.782  |  **0.826**     | **0.789**  |  0.908     |  **0.857**  |  **0.862**     |  **0.893**  |  **0.916**      |  **0.864**   | 
|  LPIPS-R1  |  0.238   |  0.042  |  0.276  |  0.159      |  0.231  |  0.068     |  0.189  |  0.112     |  0.088  |  0.251       |  0.165    | 
|  LPIPS-Ours  |  **0.159**   |  **0.021**  |  **0.204**  |  **0.126**      |  **0.153** |  **0.048**     |  **0.112**  |  **0.103**     |  **0.070**  |  **0.056**       |  **0.105**    | 
|  FID-R1    |  35.43   |  9.32   |  70.99  |  63.41      |  87.24  |  32.24     |  77.54  |  29.84     |  29.82  |  143.66      |  57.95    | 
|  FID-Ours    |  **18.72**   |  **5.92**   |  **37.23**  |  **62.21**      |  **44.36**  |  **23.77**     |  **44.30**  |  **23.39**     |  **22.77** |  **23.50**      |  **30.62**    |


R1. Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model, CVPR 2024.

R2. The Perception-Distortion Tradeoff. CVPR2018.

R3. PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration. ECCV2020.

R4. All-in-one image restoration for unknown corruption.CVPR2022.
 
R5. Controlling vision-language models for multi-task image restoration.ICLR2024.
Thanks for your reply!

1. Regarding the use of the pre-trained encoder, we describe it in the "Training and Inference Procedure" section of the paper, specifically on  L195. We apologize for any misunderstanding caused by this sentence in L126. We will revise this sentence to make our description clearer and avoid misunderstandings. Thanks for your suggestions again.

2. Since the JPEG-compressed test set contains only 29 images, we checked the size of each image and separately tested those with dimensions not divisible by 2 using a tiled mode in Restormer. Additionally, in the tiled mode, we used the grid function in BasicSR, which is a commonly used method similar to padding.

We hope our response can address these concerns.
It seems some misunderstanding regarding the sentence, "Additionally, there is insufficient motivation for the restoration tasks differently from those used in existing universal restoration models." To clarify, "those" refers to the restoration tasks, not the methods. This connects to my initial review point: "The proposed method underperforms previous works in significant degradation types such as blurry, low-light, raindrop, and rainy. The average scores might mislead the evaluation performances."


As I commented, the proposed method is interesting, but the experimental results are limited to support its contributions.
I thank the authors for their further clarifications and robust discussion.

1. The additional dialogue on distinction to previous work, with regard to incorporation-of-priors strategy, is helpful and further alleviates my concerns on this point.

2. I think we have (somewhat) converged here.

3. I agree with the authors that qualitative examples will likely further aid reader understanding on method sensitivity to this hyperparameter and would welcome such additions. 

As previously noted, my remaining concerns can be considered smaller and I believe my rating, confidence scores now reflect this accurately.
Thanks for your reply and positive rating, it means a lot to us.
We appreciate the reviewer's response, but it is hard for us to agree with your subsequent comments, particularly regarding "those used in existing universal restoration models."

Based on the reviewer's initial feedback, we have added the additional method for comparison and explained the performance of different strategies such as "top-1," "top-2," and "all." Regarding the new concern of "insufficient motivation" raised by the reviewer, our paper already provides a detailed discussion. We have also further discussed the innovative aspects raised by Reviewer 4Lsm. Please refer to that. If the reviewer believes our method is difficult to distinguish from existing universal restoration models, please provide specific references and indicate which aspects are insufficient. Otherwise, it is not convincing and we can't accept this comment.
Thanks for your patient discussion. We welcome the reviewer to discuss our paper based on fairness and objectivity.

1. Although IPT[R1] has pre-training and fine-tuning stages, it differs significantly from our method. IPT uses synthesized degraded image pairs and learns the mapping from degraded images to clear images during the pre-trained stage. This involves incorporating priors about the degradation process, which is different from the image distribution prior used in the generative models of our method. Additionally, during the fine-tuning stage, the shared parameters are still updated while our pre-trained generative model is frozen. This means that it is necessary to reload the pre-trained parameters in IPT and retrain the parameters when testing different degraded images. Our pre-trained generative model is frozen, meaning the shared parameters have the same weights when testing different degradation types. In contrast, IPT’s shared parameters have different weights during testing. With the fine-tuning stage and the need for explicit degradation categories, this method cannot even be classified as an all-in-one approach. This is why AirNet[R2] claims that they are the first to propose the all-in-one restoration task. 

2. Thanks for the reviewer's suggestions. The core issue with using terms like "multi-task," "all-in-one," or "universal" is how to define the range of degradation types included in the term "all." Currently, this range is not clearly defined in existing papers. Based on the response, the reviewer agrees with this reason. While we believe "multi-task" is a suitable term in this field, the experiments we conducted also support our claim of universal image restoration. And considering the "reader expectation management" mentioned by the reviewer, we will revise this description as we promised in the original rebuttal.

3. If the reviewer thinks the performance improvement of our 𝐾 strategy is minimal, it will be better if they can refer to the ablation studies of existing restoration methods, such as HInet[R3] and Restormer[R4]. For example, in Restormer, each innovative module has an improvement in PSNR between 0.05 and 0.2. Additionally, the reviewer compares the results of 𝐾=1 and 𝐾=2. But the model with 𝐾=1 is already an improved version of our method, not the "baseline." Our model with 𝐾=1 can handle the primary degradation in the image, and increasing 𝐾 aims to address secondary degradations in case of mixed degradation. We will add a comparison of the processing results when K has different values in the paper if we have the opportunity to submit a camera-ready version.

R1. Pre-trained image processing transformer. CVPR 2021.

R2. All-in-one image restoration for unknown corruption. CVPR2022.

R3. Hinet: Half instance normalization network for image restoration. CVPR2021.

R4. Restormer: Efficient transformer for high-resolution image restoration. CVPR2022.
I thank the authors for their further thoughts and the discussion.

1. On core contribution - I appreciate the authors' reply around using shared image priors across different image restoration tasks. Apologies if the use of the term "rebranding" was clumsy, I intend to communicate that any distinction with a core strategy of augmenting a pre-trained model with task-specific components remains somewhat subtle for this reviewer. Multi-task methods have indeed made use of 'shared image priors' for some years (e.g. [R1]), for multiple image restoration tasks. I consider the novelty of core-strategy somewhat modest, however the combination of method components do indeed differ.

2. I agree that authors experimentally cover a diverse set of image restoration tasks. On "universal" terminology; this is largely a matter of taste. If we define the word as "applicable to all cases", then one might expect similar ID / OOD performance. The comment was originally designed to help the authors with reader expectation management. I appreciate that opinions may diverge on this.

3. My concern here is largely about the statistical power of the effect size relating to K. Additionally; if we are considering PSNR deltas on the order of 10^-2, are these differences qualitatively distinct in image space? meaningful? I find the evidence relating to 'choice of K' claims not overly convincing. I acknowledge that OOD scenarios are challenging (see previous comment) and that method performance improves c.f. alternatives however strong performance, in absolute terms, would seem to evade all methods under comparison. 

In sum the authors and I seem unable to reach agreement on a subset of points, however these may not be pathological in nature. I'm happy to somewhat modify my score to reflect this.

References

R1. Chen et al. 2021. Pre-trained image processing transformer. CVPR 2021.
Thank you for your response. I still have some concerns.
1. In Line 146-147, the authors claim "Following Daclip-ir [20], we utilized the pre-trained image encoder in CLIP [35]". This led me to mistakenly believe that the author was using the original OpenAI CLIP.
2. “However, for some images, their dimensions cannot be evenly divided by 2 during the forward process. In such cases, we use a tiled mode to test these images, as shown in the JPEG degradation image in Figure 3.” For low-level vision models, this is a common issue, and the most commonly used operation is to pad the image (e.g., Restormer, NAFNet, Uformer). Why use a tiled mode? When testing your proposed model, how do you handle this kind of situation?
I appreciate the authors' efforts for the rebuttal. However, the response does not alleviate my concerns. In the case of mixed degradation, the performance difference between the Top-1 and All strategies is only 0.12 dB, and there is little performance difference between the All and Top-2 strategies. Additionally, there is insufficient motivation for the restoration tasks differently from those used in existing universal restoration models. I will maintain my original score.
I want to thank the authors for their explanations in the rebuttal. Most of my concerns are addressed thus I will keep my original score.
Thanks for the response from the reviewer. But we cannot agree with the reviewer's response.

1. Core contribution: We respectfully disagree with the reviewer's view that this is a rebranding of method terminology. 
We introduced the "multi-domain transfer learning" paradigm primarily because pre-trained generative models inherently have **shared image priors across different image restoration tasks**, which current "all-in-one" or "multi-task" image restoration methods have neither proposed nor utilized. Additionally, using parameter-efficient adapters to capture the differences between various degradation restoration tasks and avoid task conflicts is also a novel approach, which has not been explored in the image restoration field.  If the reviewer considers our method to be a "rebranding of method terminology," please provide the references on which this innovative criticism is based. Otherwise, this claim is not convincing.

2. Method description: "Multi-task image restoration" is a more appropriate term; however, aside from Daclip-IR, other papers with the same setting currently use the terms "all-in-one" or "universal." We have simply followed the common usage but are also considering adopting the more accurate term "multi-task image restoration," as suggested by the reviewer. The reviewer claims that we only trained on 10 types of degradation, but we also tested on mixed degradation and included experiments with unknown degradation during the rebuttal period. Whether in terms of the types of degradation or the complexity of degradation, we have already surpassed all-in-one methods like AirNet and PromptLR. We have covered the diversity of image restoration tasks as comprehensively as possible. From this perspective, why should it not be considered "universal"?

5. Additional results: First, please do not overlook the scenarios in which 𝐾 is used in our method. It addresses mixed degradation, which involves data distributions that were not encountered during training, and this is inherently a challenging problem. We are unsure where the reviewer’s term "near the noise level" comes from. Could you please provide more specific details? If the reviewer is referring to the data we tested, please note that our method shows improvements across four metrics and has been tested on multiple images, which avoids fluctuations based on a single image or a specific metric. Additionally, the data used in the "smoky" and "Hazy-Blurry-Noisy" experiments are also out-of-distribution. Existing image restoration methods generally struggle with this case, and even many methods have not considered such a case. Please also do not overlook the performance improvements of our method compared to other SOTA methods.
I thank the authors for the response and address their reply point-by-point. 

1. The author rebuttal states that the core technical contributions did not focus on 'technical differences'. For the reviewer, this remains a confusing strategy for the focus of technical contributions. The crux of my concern is that task conflicts and feature sharing are well understood concepts and the rebranding of method terminology ('multi-domain transfer learning') has not added significant additional problem insight, for me. 

2. The authors seem to concede that 'multi-task image restoration' is an accurate description of the work. This appears to further blur any distinction on method terminology.

3. Assuming the introduction claim is appropriately amended, I can consider this concern well addressed.

4. I can consider this concern well addressed.

5. I can consider this concern somewhat addressed and appreciate the additional experimental work. The effect of K in mixed degradation scenarios appears fairly underwhelming (ie. are we near the noise level?). Further I remain unconvinced that PSNRs of circa 12--15 on unknown degradations ('smoky') and mixed scenarios ('Hazy-Blurry-Noisy') are evidencing what might be considered succesful and pragmatic method performance.

In sum, authors address a subset of my concerns however core issues remain problematic. I therefore do not increase my score.