317 citations · 468 across the 4 of their papers we have counts for
4 papers
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
Hao Hou, Jun Xu, Yingkun Hou +3
Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on f…
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement
Hao Hou, Yingkun Hou, Yuxuan Shi +2
Retinex model has been applied to low-light image enhancement in many existing methods. More appropriate decomposition of a low-light image can help achieve better image enhancemen…
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Yingkun Hou, Jun Xu, Mingxia Liu +4
Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS pri…
STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren +5
Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives a…