Ofer Hadar

During the last decades, statistical models, such as the Ising model, have become very useful in describing solid state systems. These models excel in their simplicity and versatility. Furthermore, their results get quite often accurate experimental proofs. Leading researchers have used them successfully during the last years to restore images. A simple method, based on the Ising model, was used recently in order to restore B/W and grayscale images and achieved preliminary results. In this paper we outline first the analogy between statistical physics and image processing. Later, we present the results we have achieved using a similar, though more complex iterative model in order to get a better restoration. Moreover, we describe models which enable us to restore colored images. Additionally, we present the results of a novel method in which similar algorithms enable us to restore degraded video signals. We confront our outcomes with the results achieved by the simple algorithm and by the median filter for various kinds of noise. Our model reaches PSNR values which are 2-3 dB higher, and SSIM values which are 15%-20% higher than the results achieved by the median filter for video restoration.