Joint optimization in linear and nonlinear image restoration under the presence of different kinds of noise

The image restoration problem is considered, in the presence of mixed types of noise. An approach that exploits space-varying objective functions, which result in global, locally-varying filtering schemes for the removal of mixed noise, is introduced. The analysis is performed for both the linear and the nonlinear image formation systems. The algorithm proposed results in a degradation of the detailed structure existing in the original image. The problem of simultaneous noise removal and edge preservation is addressed. The ultimate objective is to invert the linear and the nonlinear transformations introduced by the image formation system, and remove as much long, as well as short and/or medium-tailed noise, while simultaneously preserving the detailed structure of the image. The properties of the algorithms proposed are demonstrated through restoration examples