Xu Jinchang

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: In this article, a generative adversarial network-based blurred face reconstruction method and system is presented. But the face reconstruction system is composed of an acquisition unit, a model generation unit, and a face reconstruction unit.

TL;DR: In this paper, a method for in-vivo face detection based on human face recognition was proposed, which comprises the following steps that a video including human faces is input, and the video is cut into picture sequences according to frame frequency.

TL;DR: A super-resolution (SR) method is presented, which uses three losses assigned with different weights to be regarded as optimization target and reconstructs the low resolution image with three color channels simultaneously, which shows better performance on these two tracks and benchmark datasets.

TL;DR: In this article, a deep learning-based super-resolution image reconstruction method and system is proposed, which comprises the steps of acquiring an image to be reconstructed and training data; inputting the training data into a multilayer convolutional neural network based on a residual structure for learning; reconstructing an optimal model acquired through input learning of the image to reconstruct to acquire a superresolution image.