Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior

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.

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.

Incorporating the Maximum Entropy on the Mean Framework with Kernel Error for Robust Non-Blind Image Deblurring

Non-blind deblurring is crucial in image restoration.While most previous works assume that the exact blurring kernel is known,this is often not the case in prac-tice.The blurring kernel is most likely estimated by a blind deblurring method and is not error-free.In this work,we incorporate a kernel error term into an advanced non-blind deblurring method to recover the clear image with an inaccurately estimated kernel.Based on the celebrated principle of Maximum Entropy on the Mean(MEM),the regularization at the level of the probability distribution of images is carefully com-bined with the classical total variation regularizer at the level of image/kernel.Exten-sive experiments show clearly the effectiveness of the proposed method in the pres-ence of kernel error.As a traditional method,the proposed method is even better than some of the state-of-the-art deep-learning-based methods.We also demonstrate the potential of combining the MEM framework with classical regularization approaches in image deblurring,which is extremely inspiring for other related works.