Fast and efficient image quality enhancement via desubpixel convolutional neural networks

Abstract

This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues. First, the considered FEQE performs majority of its computation in a low-resolution space. Second, the number of channels used in the convolutional layers is small which allows FEQE to be very deep. Third, the FEQE performs downsampling referred to as desubpixel that does not lead to loss of information. Experimental results on a number of standard benchmark datasets show significant improvements in image fidelity and reduction in processing time of the proposed FEQE compared to the recent state-of-the-art methods. In the PIRM 2018 challenge, the proposed FEQE placed first on the image super-resolution task for mobile devices. The code is available at https://github.com/thangvubk/FEQE.git.