Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior (Communications in Computational Physics, 2023)
Blind deblurring for color images has long been a challenging computer vision task. The intrinsic color structures within image channels have typically beendisregarded in many excellent works. We investigate employing regularizations in thehue, saturation, and value (HSV) color space via the quaternion framework in orderto better retain the internal relationship among the multiple channels and reduce colordistortions and color artifacts. We observe that a geometric spatial-feature prior utilized in the intermediate latent image successfully enhances the kernel accuracy forthe blind deblurring variational models, preserving the salient edges while decreasingthe unfavorable structures. Motivated by this, we develop a saturation-value geometric spatial-feature prior in the HSV color space via the quaternion framework forblind color image deblurring, which facilitates blur kernel estimation. An alternatingoptimization strategy combined with a primal-dual projected gradient method can effectively solve this novel proposed model. Extensive experimental results show thatour model outperforms state-of-the-art methods in blind color image deblurring by awide margin, demonstrating the effectiveness of the proposed model.