Cat-AIR: Content and Task-Aware All-in-One Image Restoration

• URL: http://arxiv.org/abs/2503.17915v1
• Date: Sun, 23 Mar 2025 03:25:52 GMT
• Title: Cat-AIR: Content and Task-Aware All-in-One Image Restoration
• Authors: Jiachen Jiang, Tianyu Ding, Ke Zhang, Jinxin Zhou, Tianyi Chen, Ilya Zharkov, Zhihui Zhu, Luming Liang,
• Abstract summary: Cat-AIR is a novel framework for textbfAnd textbfTask-aware framework for textbfImage textbfRestoration.<n>Cat-AIR incorporates an alternating spatial-channel attention mechanism that adaptively balances the local and global information for different tasks.<n>Experiments demonstrate that Cat-AIR achieves state-of-the-art results across a wide range of restoration tasks, requiring fewer FLOPs than previous methods.
• Score: 50.46278224313221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: All-in-one image restoration seeks to recover high-quality images from various types of degradation using a single model, without prior knowledge of the corruption source. However, existing methods often struggle to effectively and efficiently handle multiple degradation types. We present Cat-AIR, a novel \textbf{C}ontent \textbf{A}nd \textbf{T}ask-aware framework for \textbf{A}ll-in-one \textbf{I}mage \textbf{R}estoration. Cat-AIR incorporates an alternating spatial-channel attention mechanism that adaptively balances the local and global information for different tasks. Specifically, we introduce cross-layer channel attentions and cross-feature spatial attentions that allocate computations based on content and task complexity. Furthermore, we propose a smooth learning strategy that allows for seamless adaptation to new restoration tasks while maintaining performance on existing ones. Extensive experiments demonstrate that Cat-AIR achieves state-of-the-art results across a wide range of restoration tasks, requiring fewer FLOPs than previous methods, establishing new benchmarks for efficient all-in-one image restoration.

Related papers

• Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation [158.37640586809187]
  Restoring any degraded image efficiently via just one model has become increasingly significant. Our approach, termed AnyIR, takes a unified path that leverages inherent similarity across various degradations. To fuse the degradation awareness and the contextualized attention, a spatial-frequency parallel fusion strategy is proposed.
  arXiv  Detail & Related papers  (2025-04-19T09:54:46Z)
• Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration [109.38288333994407]
  Contrastive Prompt Learning (CPL) is a novel framework that fundamentally enhances prompt-task alignment. Our framework establishes new state-of-the-art performance while maintaining parameter efficiency, offering a principled solution for unified image restoration.
  arXiv  Detail & Related papers  (2025-04-14T08:24:57Z)
• Multi-Scale Representation Learning for Image Restoration with State-Space Model [13.622411683295686]
  We propose a novel Multi-Scale State-Space Model-based (MS-Mamba) for efficient image restoration. Our proposed method achieves new state-of-the-art performance while maintaining low computational complexity.
  arXiv  Detail & Related papers  (2024-08-19T16:42:58Z)
• HAIR: Hypernetworks-based All-in-One Image Restoration [46.681872835394095]
  Hair is a Hypernetworks-based All-in-One Image Restoration plug-and-play method. It generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically. It can significantly improve the performance of existing image restoration models in a plug-and-play manner, both in single-task and All-in-One settings.
  arXiv  Detail & Related papers  (2024-08-15T11:34:33Z)
• Restore Anything Model via Efficient Degradation Adaptation [129.38475243424563]
  RAM takes a unified path that leverages inherent similarities across various degradations to enable efficient and comprehensive restoration.<n> RAM's SOTA performance confirms RAM's SOTA performance, reducing model complexity by approximately 82% in trainable parameters and 85% in FLOPs.
  arXiv  Detail & Related papers  (2024-07-18T10:26:53Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance [12.066756224383827]
  textbfRestorer is a novel Transformer-based all-in-one image restoration model. It can handle composite degradation in real-world scenarios without requiring additional training. It is efficient during inference, suggesting the potential in real-world applications.
  arXiv  Detail & Related papers  (2024-06-18T13:18:32Z)
• Efficient Degradation-aware Any Image Restoration [83.92870105933679]
  We propose textitDaAIR, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime. By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning.
  arXiv  Detail & Related papers  (2024-05-24T11:53:27Z)
• Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration [50.81374327480445]
  We introduce a novel concept positing that intricate image degradation can be represented in terms of elementary degradation. We propose the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS) The proposed U-WADN achieves better performance while simultaneously reducing up to 32.3% of FLOPs and providing approximately 15.7% real-time acceleration.
  arXiv  Detail & Related papers  (2024-01-24T04:25:12Z)
• Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts [52.39959535724677]
  We introduce an alternative solution to improve the generalization of image restoration models. We propose AdaptIR, a Mixture-of-Experts (MoE) with multi-branch design to capture local, global, and channel representation bases. Our AdaptIR achieves stable performance on single-degradation tasks, and excels in hybrid-degradation tasks, with fine-tuning only 0.6% parameters for 8 hours.
  arXiv  Detail & Related papers  (2023-12-12T14:27:59Z)
• Prompt-based Ingredient-Oriented All-in-One Image Restoration [0.0]
  We propose a novel data ingredient-oriented approach to tackle multiple image degradation tasks. Specifically, we utilize a encoder to capture features and introduce prompts with degradation-specific information to guide the decoder. Our method performs competitively to the state-of-the-art.
  arXiv  Detail & Related papers  (2023-09-06T15:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.