Image Restoration Based on Deep Convolutional Network in Wavefront Coding Imaging System

Abstract

Wavefront coding (WFC) is a prosperous technology for extending depth of field (DOF) in the incoherent imaging system. Digital recovery of the WFC technique is a classical ill-conditioned problem by removing the blurring effect and suppressing the noise. Traditional approaches relying on image heuristics suffer from high frequency noise amplification and processing artifacts. This paper investigates a general framework of neural networks for restoring images in WFC. To our knowledge, this is the first attempt for applying convolutional networks in WFC. The blur and additive noise are considered simultaneously. Two solutions respectively exploiting fully convolutional networks (FCN) and conditional Generative Adversarial Networks (CGAN) are presented. The FCN based on minimizing the mean squared reconstruction error (MSE) in pixel space gets high PSNR. On the other side, the CGAN based on perceptual loss optimization criterion retrieves more textures. We conduct comparison experiments to demonstrate the performance at different noise levels from the training configuration. We also reveal the image quality on non-natural test target image and defocused situation. The results indicate that the proposed networks outperform traditional approaches for restoring high frequency details and suppressing noise effectively.