Fuzzy Based Adaptive Mean Filtering Technique for Removal of Impulse Noise from Images
Punyaban Patel, Bibekananda Jena, Banshidhar Majhi · 2012
This paper proposes an fuzzy based adaptive mean filtering (FBAMF) scheme to remove high density impulse noise from images. The FBAMF is a two-stage filter where, in the first stage, a fuzzy detection technique is used to differentiate between corrupted and uncorrupted pixel by calculating the membership value of each and every pixel. Then, the corrupted pixel subjected to the second stage where they are replaced by mean value of uncorrupted neighbouring pixels selected from a window adaptively. If the numbers of uncorrupted pixels in the selected window are not sufficient, a window of next higher size is chosen. Thus, window size is automatically adapted based on the density of noise in the image. As a result window size may vary pixel to pixel while filtering. Comparison shows the proposed filter effectively removes the impulse noise with significant image quality compared with conventional method such as the Standard Median Filter(SMF), Adaptive Median Filter(AMF), Progressive Switching Median Filter(PSMF) and recently proposed methods such as Efficient Decision Based Algorithm (EDBA), Improved Efficient Decision-Based Algorithm (IDBA) and fuzzy-based decision algorithm (FBDA). The visual and quantitative results show that the performance of the proposed filter in the preservation of edges and details is better even at noise level as high as 95%. The efficiency of the proposed algorithm is evaluated using different standard images.