Improved Decision Based Adaptive Threshold Median Filter for Fingerprint Image Salt and Pepper Noise Denoising

Abstract

This paper proposes an Improved Decision Based Adaptive Threshold Median Filter(IDBATMF) for fingerprint image salt and pepper noise denoising, which can more effectively remove the salt and pepper noise in fingerprint images and preserve the details of the image. The algorithm first detects noise, introduces the minimum absolute brightness difference to reflect the difference between candidate noise pixels and surrounding non-noise pixels, and innovatively sets a linear threshold, which is adapted to the local noise density and compared with the minimum absolute brightness difference to distinguish noise pixels. When removing noise, the window size used is determined by the extreme pixel density in the window, and the median of the non-extreme pixel in the window is used for replacement. The methods mentioned in this paper are compared with Standard Median Filter (SMF), Adaptive Median Filter (AMF), Modified Adaptive Median Filter (MAMF), Switching Median Filter (SWMF), Adaptive Switching Median Filter (ASMF), the Decision Based Algorithm (DBA), and the Modified Decision Based Algorithm (MDBA). The experimental results show that, compared with the existing methods, the method proposed in this paper better considers the local characteristics of the image, and has better processing effect under various noise densities.