Image Deblurring Using Various Restoration Methods

We can utilize various methods in MATLAB for restoring blurred images. These techniques include Wiener filtering, blind deconvolution, the Lucy-Richardson algorithm, and regularized image restoration. Each method possesses unique advantages and appropriate application scenarios. Wiener filtering effectively removes noise from images through frequency-domain processing using the deconvwnr function, which requires knowledge of the point spread function (PSF). Blind deconvolution recovers the original appearance of blurred images without prior PSF knowledge using the deconvblind function that iteratively estimates both the image and PSF. The Lucy-Richardson method enhances image clarity through an iterative deconvolution algorithm implemented via deconvlucy, particularly effective for Poisson noise statistics. Regularized restoration achieves optimal balance between sharpness enhancement and noise suppression using constrained optimization techniques like the deconvreg function, which incorporates regularization parameters to control solution smoothness. Therefore, based on specific requirements and image characteristics, we can select appropriate methods for blurred image restoration tasks, considering factors such as noise type, available PSF information, and computational resources.