  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.
