@Article{CiCP-33-3,
author = {Zhang, Hao and Yingying, Fang and Wong, Shing, Hok and Lihua, Li and Zeng, Tieyong},
title = {Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior},
journal = {Communications in Computational Physics},
year = {2023},
volume = {33},
number = {3},
pages = {795--823},
abstract = {<p style="text-align: justify;">Blind deblurring for color images has long been a challenging computer vision task. The intrinsic color structures within image channels have typically been
disregarded in many excellent works. We investigate employing regularizations in the
hue, saturation, and value (HSV) color space via the quaternion framework in order
to better retain the internal relationship among the multiple channels and reduce color
distortions and color artifacts. We observe that a geometric spatial-feature prior utilized in the intermediate latent image successfully enhances the kernel accuracy for
the blind deblurring variational models, preserving the salient edges while decreasing
the unfavorable structures. Motivated by this, we develop a saturation-value geometric spatial-feature prior in the HSV color space via the quaternion framework for
blind color image deblurring, which facilitates blur kernel estimation. An alternating
optimization strategy combined with a primal-dual projected gradient method can effectively solve this novel proposed model. Extensive experimental results show that
our model outperforms state-of-the-art methods in blind color image deblurring by a
wide margin, demonstrating the effectiveness of the proposed model.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2022-0226},
url = {https://global-sci.com/article/79432/saturation-value-blind-color-image-deblurring-with-geometric-spatial-feature-prior}
}