9 citations · 18 across the 5 of their papers we have counts for
6 papers
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation
Cheng Zhang, Dong Gong, Jiumei He +3
Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restora…
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
Cheng Zhang, Shaolin Su, Yu Zhu +3
In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recoveri…
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance
Rui Li, Xiantuo He, Danna Xue +5
Self-supervised depth estimation has made a great success in learning depth from unlabeled image sequences. While the mappings between image and pixel-wise depth are well-studied i…
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
Pei Wang, Wei Sun, Qingsen Yan +5
Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observatio…
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Rui Li, Qing Mao, Pei Wang +4
Self-supervised depth estimation has shown its great effectiveness in producing high quality depth maps given only image sequences as input. However, its performance usually drops…
Attention-based network for low-light image enhancement
Cheng Zhang, Qingsen Yan, Yu zhu +3
The captured images under low light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vi…