A new efficient nonlinear filter based on support vector machines for image denoising

In this work, we present a new approach for optimum design of nonlinear filters based on support vector machines. Taking advantage on the general concept of binary filters and machine learning theory, this proposed approach, is based on the concept of a new filter structured, called support vector machine filter (SVMF) and statistical data analysis. This proposed filter approach, is used as an impulsive noise image denoising. The results obtained for the application at hand show that the proposed filter outperforms a new algorithm for elimination of impulsive noise recently reported and center weighted median in the image denoising task. The proposed filter can be successfully applied for the processing of images corrupted with impulsive noise while maintaining the visual quality and a low reconstruction error.

1. INTRODUCTION

Nonlinear filters have shown a great performance in signal, image and video processing tasks when the underlying noise follows an heavy-tail distribution. In particular, Boolean filters are a class of nonlinear filters widely used that encompass stack filters, weighted median filters, among others [1], [2]. They are characterized by two basic properties: threshold decomposition and a binary operation (Boolean function) performed over the observation samples. The success of these filters is based on two intrinsic properties: edge preservation and efficient noise attenuation, being robust against impulsive noise [3].