Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring

TL;DR

This paper introduces a novel frequency domain method for estimating blur kernels in single-image blind deblurring by analyzing phase-only images, leading to improved kernel estimation and deblurring results.

Contribution

It proposes a new approach using auto-correlation of phase-only images to directly estimate high-quality blur kernels, including non-uniform blur, which is a significant advancement over prior priors-based methods.

Findings

Effective kernel estimation from phase-only images.

Improved deblurring results on synthetic and real data.

Extension to handle spatially varying blur.

Abstract

The image blurring process is generally modelled as the convolution of a blur kernel with a latent image. Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. Unlike existing approaches which focus on approaching the problem by enforcing various priors on the blur kernel and the latent image, we are aiming at obtaining a high quality blur kernel directly by studying the problem in the frequency domain. We show that the auto-correlation of the absolute phase-only image can provide faithful information about the motion (e.g. the motion direction and magnitude, we call it the motion pattern in this paper.) that caused the blur, leading to a new and efficient blur kernel estimation approach. The blur kernel is then refined and the sharp image is estimated by solving an optimization problem by enforcing a regularization on the blur kernel and the latent image. We further extend our approach to handle non-uniform blur, which involves spatially varying blur kernels. Our approach is evaluated extensively on synthetic and real data and shows good results compared to the state-of-the-art deblurring approaches.