A novel improved median filter for salt-and-pepper noise from highly corrupted images

This paper proposes a novel improved median filter algorithm for the images highly corrupted with salt-and-pepper noise. Firstly all the pixels are classified into signal pixels and noisy pixels by using the Max-Min noise detector. The noisy pixels are then separated into three classes, which are low-density, moderate-density, and high-density noises, based on the local statistic information. Finally the weighted 8-neighborhood similarity function filter, the 5×5 median filter and the 4-neighborhood mean filter are adopted to remove the noises for the low, moderate and high level cases, respectively. In experiment, the proposed algorithm is compared with three typical methods, named Standard Median filter, Extremum Median filter and Adaptive Median filter, respectively. The validation results show that the proposed algorithm has better performance for capabilities of noise removal, adaptivity, and detail preservation, especially effective for the cases when the images are extremely highly corrupted.