Nimisha Thekke Madam

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: An end-to-end deblurring network designed specifically for a class of data that learns a strong prior on the clean image domain using adversarial loss and maps the blurred image to its clean equivalent and imposes a scale-space gradient error with an additional gradient module.

TL;DR: The proposed network, called enhanced perceptual super-resolution network (EPSR), is trained with a combination of mean squared error loss, perceptual loss, and adversarial loss and achieves the state-of-the-art trade-off between distortion and perceptual quality while the existing methods perform well in either of these measures alone.

TL;DR: In this article, an enhanced perceptual super-resolution network (EPSR) was proposed, which is trained with a combination of mean squared error loss, perceptual loss, and adversarial loss.