most citedHAT: Hybrid Attention Transformer for Image Restoration

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cs.CV202520 cited

HAT: Hybrid Attention Transformer for Image Restoration

Xiangyu Chen, Xintao Wang, Wenlong Zhang +4

Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only…

cs.CV2024

A Preliminary Exploration Towards General Image Restoration

Xiangtao Kong, Jinjin Gu, Yihao Liu +4

Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being app…

cs.CV2024

Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation

Xiangyu Chen, Zheyuan Li, Zhengwen Zhang +6

Modern displays can render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most resources are still in standard dynamic range (SDR). Therefore, tra…

cs.CV2024

Learning A Low-Level Vision Generalist via Visual Task Prompt

Xiangyu Chen, Yihao Liu, Yuandong Pu +4

Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoratio…

cs.CV2024

A Comparative Study of Image Restoration Networks for General Backbone Network Design

Xiangyu Chen, Zheyuan Li, Yuandong Pu +4

Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An i…

cs.CV2024

Unifying Image Processing as Visual Prompting Question Answering

Yihao Liu, Xiangyu Chen, Xianzheng Ma +4

Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally…