Shixiang Wu

TL;DR: ESRGAN as mentioned in this paper improves the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery, and won the first place in the PIRM2018-SR Challenge (region 3).

TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.

TL;DR: This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.

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.