8 citations · 27 across the 10 of their papers we have counts for
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Multi-Scale Representation Learning for Image Restoration with State-Space Model
Yuhong He, Long Peng, Qiaosi Yi +2
Image restoration endeavors to reconstruct a high-quality, detail-rich image from a degraded counterpart, which is a pivotal process in photography and various computer vision syst…
Textual Prompt Guided Image Restoration
Qiuhai Yan, Aiwen Jiang, Kang Chen +3
Image restoration has always been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, "all-in-one…
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Juncheng Li, Hanhui Yang, Qiaosi Yi +4
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of paramete…
Structure-Preserving Deraining with Residue Channel Prior Guidance
Qiaosi Yi, Juncheng Li, Qinyan Dai +3
Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognitio…
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Qinyan Dai, Juncheng Li, Qiaosi Yi +2
Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effec…
Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
Qiaosi Yi, Juncheng Li, Faming Fang +2
Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great suc…
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background
Qiaosi Yi, Yunxing Liu, Aiwen Jiang +3
Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current res…
Disentangle Perceptual Learning through Online Contrastive Learning
Kangfu Mei, Yao Lu, Qiaosi Yi +3
Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. Perceptual learning approaches like perceptual loss are em…