Image restoration techniques in spatial domain and frequency domains / Osamah Jasim Madhloom

A digital image is a numeric representation of a two-dimensional image. A recorded image is often contaminated with noise. Hence, image restoration is a fundamental research topic in the realm of image to obtain an optimal estimate of the original image given the degraded image. In this study, Direct Inverse Filter, Wiener filter, and Complex Wavelet filter techniques are applied to eliminate the noise, thereby improving the quality of the restored image. They are analyzed, derived, and implemented using MATLAB software for reconstructing the degraded image. Two types of noise, Gaussian noise and Salt & Pepper noise with different levels of noise are used to contaminate the original image. Then, image quality metrics, namely; mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) are applied to measure the quality of the restored images using the aforementioned image restoration techniques. Experimental and simulation results show that Constrained Complex wavelet filter is the best-performing image restoration technique followed by Wiener filler, and finally Direct Inverse filter