activity
20162020
most citedDeep Convolution Networks for Compression Artifacts Reduction

72 citations · 157 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV202043 cited

Efficient Image Super-Resolution Using Pixel Attention

Hengyuan Zhao, Xiangtao Kong, Jingwen He +2

This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective ne…

cs.CV20205 cited

Conditional Sequential Modulation for Efficient Global Image Retouching

Jingwen He, Yihao Liu, Yu Qiao +1

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation.…

cs.CV202017 cited

Understanding Deformable Alignment in Video Super-Resolution

Kelvin C. K. Chan, Xintao Wang, Ke Yu +2

Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is incr…

cs.CV20205 cited

Enhanced Quadratic Video Interpolation

Yihao Liu, Liangbin Xie, Li Siyao +3

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many lea…

cs.CV20194 cited

Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

Jingwen He, Chao Dong, Yu Qiao

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most…

cs.CV201672 cited

Deep Convolution Networks for Compression Artifacts Reduction

Ke Yu, Chao Dong, Chen Change Loy +1

Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking art…