An end‐to‐end perceptual enhancement method for UHD portrait images

Abstract

Advances in equipment enable photographers to take ultra-high-definition (UHD) photos, and photo retouching enables them to create an impressive image by artistically adjusting and enhancing the brightness, color etc. However, such image enhancement is an artistic and challenging task, which requires a lot of relevant experience and technology. Thus, using an automated algorithm is an attractive option. In recent years, deep learning has made great progress in image processing, which motivated the authors to explore this application in UHD image enhancement task. In this paper, an end-to-end perceptual enhancement method for UHD portrait images is proposed. Since the artistry of images comes from human perception, including the perception of exposure, structure, color etc., an image preprocessing method is proposed which can help the network learn the brightness change from the original image to the target image more accurately. Then, a composite perceptual loss function is proposed that combines global, structural, and color losses. This loss allows the network to simultaneously optimize the distance and perceptual similarity to a given target image during back-propagation. To preserve the details of the UHD image and reduce the number of network calculations and parameters, a Perceptual Enhancement Network (called PEN) based on dilated convolution is proposed. Extensive experiments on the datasets show that the method excels in baselines in both subjective and objective evaluations.