Abstract
In this article, we propose a novel blind image deconvolution method developed within the Bayesian framework. We concentrate on the restoration of blurred photographs taken by commercial cameras to show its effectiveness. The proposed method is based on a non-convex l p quasi norm with 0<p<1 that is used for the image, and a total variation (TV) based prior that is utilized for the blur. Bayesian inference is carried out by utilizing bounds for both the image and blur priors using a majorization-minimization principle. Maximum a posteriori estimates of the unknown image, blur and model parameters are calculated. Experimental results (i.e., restorations of more than 30 blurred photographs) are presented to demonstrate the advantage of the proposed method compared to existing ones.
Highlights
Blind image deconvolution (BID) refers to the process of estimating both the original image and the blur from the degraded noisy image observation by using partial information about the imaging system
Blind image deconvolution algorithms represent a valuable tool that can be used for improving image quality without requiring complicated calibrations of the real-time image acquisition and processing system
5 Experimental results we present the experimental results obtained by the use of the proposed algorithm
Summary
Blind image deconvolution (BID) refers to the process of estimating both the original image and the blur from the degraded noisy image observation by using partial information about the imaging system. Blind image deconvolution algorithms represent a valuable tool that can be used for improving image quality without requiring complicated calibrations of the real-time image acquisition and processing system (i.e., medical imaging, videoconferencing, space exploration, x-ray imaging, etc.). Astronomical imaging is one of the primary applications of blind image deconvolution algorithms [1,2]. Blind image deconvolution is used for improving the quality of the Poisson distributed film grain noise present in the blurred X-rays, mammograms, and digital
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