2 citations · 4 across the 6 of their papers we have counts for
6 papers
Neural Degradation Representation Learning for All-In-One Image Restoration
Mingde Yao, Ruikang Xu, Yuanshen Guan +2
Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch betwee…
Mutual-Guided Dynamic Network for Image Fusion
Yuanshen Guan, Ruikang Xu, Mingde Yao +2
Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information whi…
Generalized Lightness Adaptation with Channel Selective Normalization
Mingde Yao, Jie Huang, Xin Jin +4
Lightness adaptation is vital to the success of image processing to avoid unexpected visual deterioration, which covers multiple aspects, e.g., low-light image enhancement, image r…
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Zhihong Pan, Baopu Li, Dongliang He +5
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and dow…
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
Ruikang Xu, Mingde Yao, Chang Chen +2
Existing methods for spectral reconstruction usually learn a discrete mapping from RGB images to a number of spectral bands. However, this modeling strategy ignores the continuous…
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Wenhao Wu, Yuxiang Zhao, Yanwu Xu +8
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal mode…