Empowering Image Recovery_ A Multi-Attention Approach

• URL: http://arxiv.org/abs/2404.04617v2
• Date: Tue, 9 Apr 2024 08:20:08 GMT
• Title: Empowering Image Recovery_ A Multi-Attention Approach
• Authors: Juan Wen, Yawei Li, Chao Zhang, Weiyan Hou, Radu Timofte, Luc Van Gool,
• Abstract summary: Diverse Restormer (DART) is an image restoration method that integrates information from various sources to address restoration challenges. DART employs customized attention mechanisms to enhance overall performance. evaluation across five restoration tasks consistently positions DART at the forefront.
• Score: 96.25892659985342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, feature dimensions, and positional dimensions) to address restoration challenges. While Transformer models have demonstrated excellent performance in image restoration due to their self-attention mechanism, they face limitations in complex scenarios. Leveraging recent advancements in Transformers and various attention mechanisms, our method utilizes customized attention mechanisms to enhance overall performance. DART, our novel network architecture, employs windowed attention to mimic the selective focusing mechanism of human eyes. By dynamically adjusting receptive fields, it optimally captures the fundamental features crucial for image resolution reconstruction. Efficiency and performance balance are achieved through the LongIR attention mechanism for long sequence image restoration. Integration of attention mechanisms across feature and positional dimensions further enhances the recovery of fine details. Evaluation across five restoration tasks consistently positions DART at the forefront. Upon acceptance, we commit to providing publicly accessible code and models to ensure reproducibility and facilitate further research.

Related papers

• Dilated Strip Attention Network for Image Restoration [5.65781374269726]
  We propose a dilated strip attention network (DSAN) for image restoration. By employing the DSA operation horizontally and vertically, each location can harvest the contextual information from a much wider region. Our experiments show that our DSAN outperforms state-of-the-art algorithms on several image restoration tasks.
  arXiv  Detail & Related papers  (2024-07-26T09:12:30Z)
• How Powerful Potential of Attention on Image Restoration? [97.9777639562205]
  We conduct an empirical study to explore the potential of attention mechanisms without using FFN. We propose Continuous Scaling Attention (textbfCSAttn), a method that computes attention continuously in three stages without using FFN. Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
  arXiv  Detail & Related papers  (2024-03-15T14:23:12Z)
• Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild [57.06779516541574]
  SUPIR (Scaling-UP Image Restoration) is a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations.
  arXiv  Detail & Related papers  (2024-01-24T17:58:07Z)
• SPIRE: Semantic Prompt-Driven Image Restoration [66.26165625929747]
  We develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework. Our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength. Our experiments demonstrate the superior restoration performance of SPIRE compared to the state of the arts.
  arXiv  Detail & Related papers  (2023-12-18T17:02:30Z)
• Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration [58.11518043688793]
  MPerceiver is a novel approach to enhance adaptiveness, generalizability and fidelity for all-in-one image restoration. MPerceiver is trained on 9 tasks for all-in-one IR and outperforms state-of-the-art task-specific methods across most tasks.
  arXiv  Detail & Related papers  (2023-12-05T17:47:11Z)
• 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)
• ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions [28.956280590967808]
  Our architecture is based on a transformer with a novel attention mechanism. Our key idea is to sparsify the transformer's attention matrix at high resolutions, guided by dense attention extracted at lower image resolutions. We present qualitative and quantitative results, along with user studies, demonstrating the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-05-24T17:39:53Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• Image super-resolution reconstruction based on attention mechanism and feature fusion [3.42658286826597]
  A network structure based on attention mechanism and multi-scale feature fusion is proposed. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-08T11:20:10Z)

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.