Image Restoration Using Adaptive Nonlinear Techniques

Image restoration is the process of finding an approximation to the degradation process and find appropriate inverse process to estimate the original image.An iterative restoration technique(Tikhonov method) was adapted.The adapted filter was designed for restoring RGB satellite images that are blurred with space-invariant point spread function, Gaussian function, and corrupted with additive noise, and salt & pepper noise.Different degradation parameters, i.e. different signal to noise ratio were considered and different noise density.The results using an adaptive filter were compared,quantitatively, with different types of conventional restoration techniques,(such as inverse filter, Least-Squares filter (Wiener Filter), and Constrained Least-Squares filter) using Mean Square Error (MSE).Results show that The Mean Square Error of the restored images decreases with increasing the number of iteration until the result convergence. Also the ratio of the MSE of the degraded image to the restored image will increase with decreasing SNR for Gaussian noise,and with increasing noise density for salt and pepper noise respectively,then Results show this method has better performance for restoring the degraded images, especially for low signal to noise  ratio, and for high noise density.