Blind Restoration Algorithm Using Residual Measures for Motion-Blurred Noisy Images

Abstract

Abstract Image de-blurring is an inverse problem whose intent is to recover an image from the image affected badly with different environmental conditions. Usually, blurring can happen in various ways; however, de-blurring from a motion problem with or without noise can pose an important problem that is difficult to solve with less computation task. The quality of the restored image in iterative methods of blind motion de-blurring depends on the regularization parameter and the iteration number, which can be automatically or manually stopped. Blind de-blurring and restoration employing image de-blurring and whiteness measures are proposed in this paper to automatically decide the number of iterations. The technique has three modules, namely image de-blurring module, whiteness measures module, and image estimation module. New whiteness measures of hole entropy and mean-square contingency coefficient have been proposed in the whiteness measures module. Initially, the blurred image is de-blurred by the employment of edge responses and image priors using point-spread function. Later, whiteness measures are computed for the de-blurred image and, finally, the best image is selected. The results are obtained for all eight whiteness measures by employing evaluation metrics of increase in signal-to-noise ratio (ISNR), mean-square error, and structural similarity index. The results are obtained from standard images, and performance analysis is made by varying parameters. The obtained results for synthetically blurred images are good even under a noisy condition with ΔISNR average values of 0.3066 dB. The proposed whiteness measures seek a powerful solution to iterative de-blurring algorithms in deciding automatic stopping criteria.