Blind Image Deblurring with FFT-ReLU Sparsity Prior

Abstract

Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. It is a small data problem since the key challenge lies in estimating the unknown degrees of blur from a single image or limited data instead of learning from large datasets. The solution depends heavily on developing algorithms that effectively model the image degradation process. We introduce a method that leverages a prior which targets the blur kernel to achieve effective deblurring across a wide range of image types. In our extensive empirical analysis our algorithm achieves results that are competitive with the state-of-the-art blind image deblurring algorithms and it offers up to two times faster inference making it a highly efficient solution.