Adaptive Impulse Noise Removal Using a Cost Function Based Optimal Partitioning

This paper presents a new impulse noise detection and removal technique based on applying dynamic optimal partitioning (OP) to a set of neighborhoods of a pixel whose noise identity is in question. Using the nature of the impulse noise, and by gathering collaborating information from different directions around it, a pixel is deemed either noisy or normal. If the pixel is classified as noise, then a median-based noise filtering technique, or any other appropriate filtering technique, is applied; otherwise, the pixel is considered normal and left unaltered. The noise detection algorithm uses an effective dynamic optimal partitiong technique that incorporates a noise-based cost function and works for any size of neighborhood without any major algorithmic adjustments. Different cost functions are introduced for the algorithm with simulation results that show the detector´s effectiveness in the presence of low to moderate impulse noise levels.