Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

I. Introduction

Impulse noise is caused by malfunctioning pixels in camera sensors, faulty memory locations in hardware, or transmission in a noisy channel (see [1], for instance). Two common types of impulse noise are the salt-and-pepper noise and the random-valued noise. For images corrupted by salt-and-pepper noise (respectively, random-valued noise), the noisy pixels can take only the maximum and the minimum values (respectively, any random value) in the dynamic range. There are many works on the restoration of images corrupted by impulse noise (see, for instance, the nonlinear digital filters reviewed in [2]). The median filter was once the most popular nonlinear filter for removing impulse noise because of its good denoising power [1] and computational efficiency [3]. However, when the noise level is over 50%, some details and edges of the original image are smeared by the filter [4].