A new blind restoration method applying Real-coded genetic algorithm (RcGA) will be proposed, and this method will be proven valid for the blurred image restoration with unidentified degradation in the experiments. In this restoration method, the degraded and blurred image is going to get restricted to the images possible to be expressed in the point spread function (PSF), then the restoration filter for this degraded image, which is also the 2-dimentional inverse filter, will be searched among several points applying RcGA. The method will enable to seek efficiently among vast solution space consists of numeral coefficient filters. And perceiving the essential features of the spectrum in the frequency space, an evaluation function will be proposed. Also, it will be proposed to apply the Rolling-ball transform succeeding an appropriate Gaussian degrade function against the dual degraded image with blur convoluting impulse noise. By above stated features of this restoration method, it will enable to restore the degraded image closer to the original within a practical processing time. Computer simulations verify this method for image restoration problem when the factors causing image distortions are not identified.

A new image restoration filter based upon a physical model of an image degradation is constructed. By means of this filter, signal dependent noise and blur can be suppressed. In particular, image degradation noise can be modeled in generalized form. Noise suppression and deblurring are performed separately. This filter has additional applications when used in conjunction with the degradation model, such as real photographic images and photoelectronic images. Simulation results show that this filter gives a superior performance in restoring an image degraded by signal dependent noise and blur.