Massively parallel image restoration

An image restoration model that performs piecewise-constant restorations on images corrupted with very high noise levels is presented. The model uses a sigmoid nonlinearity at each pixel site to produce a restoration with sharp boundaries without using an explicit line process. The restoration is produced efficiently using a stochastic search procedure at constant temperature that typically requires less than 50 iterations. The model is able to restore images with up to 70% of the pixels corrupted with noise. The model is massively parallel with local neighbor interactions (four nearest neighbors), and it can be implemented on a large parallel computer or a VLSI chip