Self-Supervised and Supervised Image Enhancement Networks with Time-Shift Module

Abstract

Enhancing image quality provides more interpretability for both human beings and machines. Traditional image enhancement techniques work well for specific uses, but they struggle with images taken in extreme conditions, such as varied distortions, noise, and contrast deformations. Deep-learning-based methods produce superior quality in enhancing images since they are capable of learning the spatial characteristics within the images. However, deeper models increase the computational costs and require additional modules for particular problems. In this paper, we propose self-supervised and supervised image enhancement models based on the time-shift image enhancement method (TS-IEM). We embedded the TS-IEM into a four-layer CNN model and reconstructed the reference images for the self-supervised model. The reconstructed images are also used in the supervised model as an additional layer to improve the learning process and obtain better-quality images. Comprehensive experiments and qualitative and quantitative analysis are performed using three benchmark datasets of different application domains. The results showed that the self-supervised model could provide reasonable results for the datasets without reference images. On the other hand, the supervised model outperformed the state-of-the-art methods in quantitative analysis by producing well-enhanced images for different tasks.