Locally adaptive image filtering based on learning with clustering

Image filtering or denoising is a problem widely addressed in optical, infrared and radar remote sensing data processing. Although a large number of methods for image denoising exist, the choice of a proper, efficient filter is still a difficult problem and requires wide a priori knowledge. Locally adaptive filtering of images is an approach that has been widely investigated and exploited during recent 15 years. It has demonstrated a great potential, However, there are still some problems in design of locally adaptive filters that is generally too heuristic. This paper puts forward a new approach to get around this shortcoming. It deals with using learning with clustering in order to make the procedure of locally adaptive filter design more automatic and less subjective. The performance of this approach to learning and locally adaptive filtering has been tested for mixed Gaussian multiplicative+impulse noise environment. Its advantages in comparison to another learning methods and the efficiency of the considered component filters is demonstrated by both numerical simulation data and real-life radar image processing examples.