3D-image restoration technique using Genetic Algorithm to solve blurring problems of images

Abstract

Most images may not be sharp and clear due to various reasons like noise interference and is said to be in a blurred condition. Image de-blurring is fundamental in making pictures sharp and useful. Normally, along with the input blurred image, Point Spread Function (PSF) of the original image is required for the process of restoration and de-blurring. In this paper, we introduce a technique for image restoration by Richardson–Lucy algorithm where the optimised PSF is generated by the use of Genetic Algorithm (GA). Use of optimised PSF ensures that our proposed technique does not need the original image for the de-blurring purpose and can be greatly beneficial in the real time scenario cases. The dataset used for the evaluation of the proposed technique are real 3D images and the evaluation metrics used are peak signal-to-noise ratio (PSNR), Second-Derivative like Measure of Enhancement (SDME) and mean squared error (MSE). The technique is compared with existing techniques such as de-convolution method, regularisation filter, Wiener filter and Richardson–Lucy algorithm. From the results, we can observe that our proposed technique has achieved higher PSNR and SDME values and lower MSE values when compared with other techniques. We have achieved average PSNR of 70·94, SDME of 71·46 and MSE of 0·0063. The values obtained show the superior performance of the proposed technique.