Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
This addresses the challenge of image deblurring for applications like photography and computer vision, but it is incremental as it builds on existing kernel estimation methods with a new frequency-domain approach.
The paper tackled the problem of single-image blind deblurring by proposing a method to estimate blur kernels directly in the frequency domain using auto-correlation of absolute phase-only images, achieving good results compared to state-of-the-art approaches on synthetic and real data.
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.