Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring

Abstract

The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on genetic algorithm and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.