10 citations · 22 across the 3 of their papers we have counts for
9 papers
Unfolding Taylor's Approximations for Image Restoration
Man Zhou, Zeyu Xiao, Xueyang Fu +3
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering…
Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
Zhenqi Fu, Xueyang Fu, Yue Huang +1
To improve the quality of underwater images, various kinds of underwater image enhancement (UIE) operators have been proposed during the past few years. However, the lack of effect…
Real-world Person Re-Identification via Degradation Invariance Learning
Yukun Huang, Zheng-Jun Zha, Xueyang Fu +2
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On…
Noise2Blur: Online Noise Extraction and Denoising
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
We propose a new framework called Noise2Blur (N2B) for training robust image denoising models without pre-collected paired noisy/clean images. The training of the model requires on…
Rain O'er Me: Synthesizing real rain to derain with data distillation
Huangxing Lin, Yanlong Li, Xinghao Ding +3
We present a supervised technique for learning to remove rain from images without using synthetic rain software. The method is based on a two-stage data distillation approach: 1) A…
A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal
Huangxing Lin, Xueyang Fu, Changxing Jing +2
Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A^2N…