Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images

TL;DR

This paper compares the effectiveness of three non-blind deblurring methods—Wiener, Lucy-Richardson, and regularized deconvolution—on noisy blurred images with salt-and-pepper noise, using simulated motion and Gaussian blurring.

Contribution

It provides a comparative analysis of non-blind deblurring techniques on noisy images, including the impact of denoising prior to deblurring.

Findings

Wiener deconvolution performs best on noisy images with prior denoising.

Denoising before deblurring improves results across all methods.

Regularized deconvolution shows robustness against salt-and-pepper noise.

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-and-pepper 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.