Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images
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© 2022 by IJCTT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Poorna Banerjee Dasgupta
DOI :  10.14445/22312803/IJCTT-V70I3P101

How to Cite?

Poorna Banerjee Dasgupta, "Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images," International Journal of Computer Trends and Technology, vol. 70, no. 3, pp. 1-8, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I3P101

Abstract
Image blurring refers to the degradation of an image wherein the image’s overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-andpepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images.

Keywords
Deconvolution, Image blurring, Noise, Nonblind image deblurring, Point spread function.

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