Blind Deconvolution for Color Images Using Normalized Quaternion Kernels

TL;DR

This paper introduces a novel quaternion-based approach for blind deconvolution of color images, effectively capturing inter-channel relationships and improving deblurring quality over traditional methods.

Contribution

It proposes a new normalized quaternion kernel and fidelity term specifically designed for color image blind deconvolution, addressing limitations of prior channel-separate techniques.

Findings

Effectively removes artifacts in color image deblurring

Significantly improves deblurring performance on real datasets

Preserves image intensity during deconvolution

Abstract

In this work, we address the challenging problem of blind deconvolution for color images. Existing methods often convert color images to grayscale or process each color channel separately, which overlooking the relationships between color channels. To handle this issue, we formulate a novel quaternion fidelity term designed specifically for color image blind deconvolution. This fidelity term leverages the properties of quaternion convolution kernel, which consists of four kernels: one that functions similarly to a non-negative convolution kernel to capture the overall blur, and three additional convolution kernels without constraints corresponding to red, green and blue channels respectively model their unknown interdependencies. In order to preserve image intensity, we propose to use the normalized quaternion kernel in the blind deconvolution process. Extensive experiments on real datasets of blurred color images show that the proposed method effectively removes artifacts and significantly improves deblurring effect, demonstrating its potential as a powerful tool for color image deconvolution.