An Impulse Noise Removal Algorithm by Considering Region-Wise Property for Color Image

Impulse noise removal is one of the important image preprocessing techniques since the noise will lead the image processing procedures into an unexpected direction. The candidate-oriented strategy that detects the corrupted pixels (noise candidates) and then updates the intensity value of those pixels can achieve better performance than the brute-force strategy. However, conventional noise detection algorithms determine noises based on the pixel-wise relationship among a fixed sized observing window, and thus it will misclassify the normal edge and detailed pixels into the noises. In this paper, a novel region feature is presented to avoid the misclassification problem. The noises pixels are treated as the small-sized regions, and labeled by the multi-scale connected component labeling algorithm. In this way, the region size can be considered as a clue during the noise detection procedure. This newly developed region feature can be easily utilized to the current noise removal algorithms. The preliminary study results show that the number of misclassification of ROAD algorithm is dramatically reduced when considering the region feature, and thus the performance of conventional impulse noise removal algorithm can improved accordingly.