Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

Advanced image sensors with high resolution are now being developed for specially purposed electro-optical systems, with research focused on robust image quality performance in terms of super resolution and noise removal under various environmental conditions. Recently, machine-learning and deep-learning methods have been studied as the best practical techniques for restoration to improve the deteriorated image quality of sensors. However, these methods show limitations and side effects of image degradation such as image non-uniformity. In this paper, we analyze and randomly generate additive white Gaussian noise, non-uniform line noise, and dark saturation as representative image degradations. We then propose an advanced U-net model based on global and local residual learning in order to restore complexly deteriorated images. The proposed method shows unparalleled performance compared to alternative models and previous studies. In particular, various complex noise components are minimized and improved with equal quality so that variation between sequential images is minimized. These findings leverage mutual corroboration of quantitative and qualitative evaluation metrics. In the future, the proposed model is expected to contribute to a wide range of field applications such as defense, surveillance, and video media for image quality enhancement technologies.