An Improved Blind Deconvolution for Restoration of Blurred Images Using Ringing Removal Processing

Abstract

One of the most difficult challenges in image processing is restoring a defocused image by reducing blur and noise. Blurring characterizes image deterioration, and recovery is accomplished by point spread function estimation and ideal image estimation processing was repeated. Ringing, or wavelike artifacts that arise along strong edges, is a difficult challenge in latent image restoration. Therefore, this paper will introduce an improved blind deconvolution for restoring blurry images using ringing removal process. This paper provides an improved deconvolution technique that uses blur kernel prediction based on dark channels before achieving clear image recovery. To hold picture information and beautify the rims of the ringing impact created at some point of the authentic clean picture recuperation process, an easy bilateral clear out is used. The ringing removal method L₀ regularization is used with the restoration process, which can estimate a sharper image. By removing the difference map from the final deconvolution result, it is possible to get a clearer picture without ringing. Finally, the results are presented in terms of performance parameters such as signal-to-noise ratio (SNR), mean squared error (MSE), and peak signal-to-noise ratio (PSNR).The results show that the performance parameters of the improved blind deconvolution model are superior compared to existing image blur removal algorithms.