State sharing methods in statistical fluctuation for image restoration

This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with tau state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with tau state. The n images with tau state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.