Blind Deconvolution for Color Images Using Normalized Quaternion Kernels
This addresses the problem of color image deblurring for computer vision applications, offering a domain-specific improvement over methods that ignore color relationships.
The paper tackles blind deconvolution for color images by proposing a novel quaternion fidelity term and normalized quaternion kernels to model color channel interdependencies, with experiments showing effective artifact removal and improved deblurring on real datasets.
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.