35 citations · 38 across the 2 of their papers we have counts for
7 papers · 1 filter
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
Haoyu Chen, Wenbo Li, Jinjin Gu +7
Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual…
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks
Boyang Zheng, Jinjin Gu, Shijun Li +1
The success of large language models (LLMs) has fostered a new research trend of multi-modality large language models (MLLMs), which changes the paradigm of various fields in compu…
Dual Aggregation Transformer for Image Super-Resolution
Zheng Chen, Yulun Zhang, Jinjin Gu +3
Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dime…
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
Ruofan Zhang, Jinjin Gu, Haoyu Chen +3
Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate…
Masked Image Training for Generalizable Deep Image Denoising
Haoyu Chen, Jinjin Gu, Yihao Liu +5
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method f…
Rethinking Alignment in Video Super-Resolution Transformers
Shuwei Shi, Jinjin Gu, Liangbin Xie +3
The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equ…