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Found 4 papers, 3 papers with code
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch.
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement
Then a minimum fusion strategy on the results of these two transforms is utilized to achieve more natural illumination component enhancement.
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance.
STAR: A Structure and Texture Aware Retinex Model
A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.
