14 citations · 32 across the 5 of their papers we have counts for
5 papers
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
Yiyang Shen, Mingqiang Wei, Sen Deng +5
The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such…
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Sen Deng, Yidan Feng, Mingqiang Wei +5
We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compel…
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
Yiyang Shen, Yidan Feng, Sen Deng +4
Rain severely hampers the visibility of scene objects when images are captured through glass in heavily rainy days. We observe three intriguing phenomenons that, 1) rain is a mixtu…
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
Sen Deng, Mingqiang Wei, Jun Wang +3
Image deraining is a fundamental, yet not well-solved problem in computer vision and graphics. The traditional image deraining approaches commonly behave ineffectively in medium an…
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
Luming Liang, Sen Deng, Lionel Gueguen +3
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer…