Ruicheng Feng

TL;DR: The 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) is reviewed with a focus on proposed solutions and results and the state-of-the-art in real-world single image super- resolution.

TL;DR: This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.

TL;DR: In this paper, the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones was presented, where participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4.

TL;DR: A simple learning principle MixUp is introduced to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples and this strategy proves to be successful in reducing the biases of data.

TL;DR: Zhang et al. as mentioned in this paper introduced a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples, and proposed a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity.