Adaptive image restoration of sigma filter using local statistics and human visual characteristics

The sigma filter is a nonlinear scheme for modifying an average (mean) filter to improve its edge-preserving characteristic. However, this filter is susceptible to impulsive noise, such as BSC (binary symmetric channel) noise. In this letter, a simple adaptive algorithm for a sigma filter is presented using local image statistics and human visual characteristics to compensate for its drawbacks. Experimental results for an image degraded by BSC noise show that the proposed algorithm has much better performance than nonadaptive ones both on SNR gain and on subjective image quality