Learning Deep Non-Blind Image Deconvolution Without Ground Truths

Abstract

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a blurred one, given an associated blur kernel. Most existing deep neural networks for NBID are trained over many ground truth (GT) images, which limits their applicability in practical applications such as microscopic imaging and medical imaging. This paper proposes an unsupervised deep learning approach for NBID which avoids accessing GT images. The challenge raised from the absence of GT images is tackled by a self-supervised reconstruction loss that approximates its supervised counterpart well. The possible errors of blur kernels are addressed by a self-supervised prediction loss based on intermediate samples as well as an ensemble inference scheme based on kernel perturbation. The experiments show that the proposed approach provides very competitive performance to existing supervised learning-based methods, no matter under accurate kernels or erroneous kernels.