Automatic detection and removal of high‐density impulse noises. Issue 2 (1st February 2015)

• Record Type: Journal Article
• Authors: Bai, Tian; Tan, Jieqing
• Is Part Of: IET image processing. Volume 9:Issue 2(2015)
• Page Start: 162
• Page End: 172
• Publication Date: 2015-02-01
• Subjects: image denoising -- impulse noise -- interpolation -- nonlinear filters; automatic impulse detector -- local mean and variance -- massive image corrupted pixels -- human observing noisy image cognitive process -- Newton–Thiele filter -- NTF -- Newton–Thiele rational interpolation -- linear interpolation algorithm -- visual quality -- LMVD -- high‐density impulse noises removal -- impulse noise automatic detection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0286
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study presents a novel method for automatic detection and removal of high‐density impulse noises. The method consists of two parts: the impulse detection part and the impulse noise removal part. In impulse detection part, an automatic detector based on local mean and variance (LMVD) is presented, which can automatically pick out noisy image from massive images and output corrupted grey levels. The detector utilises LMVD of the neighbourhood of corrupted pixels to simulate the cognitive processes of human observing noisy image. In impulse noise removal part, the Newton–Thiele filter (NTF) instead of median filter is applied to remove impulse noise. The process to construct NTF can be divided into two steps: setting up the grid and constructing the Newton–Thiele's rational interpolation on the grid. First, eight adjacent pixels of the corrupted centre pixel are used to construct the two‐dimensional grid. If a pixel in the grid is corrupted, a four‐direction linear interpolation algorithm will be performed to provide a rough estimate to the corrupted pixel. Second, the corrupted centre pixel value will be updated by Newton–Thiele's rational interpolation on the grid. The NTF has better robustness than existing filters because it does not need to adjust window size or other parameters. Simulations reveal that the proposed detector and filter have perfect performance in terms of both quantitative evaluation and visual quality, especially it can remove the impulse noise effectively even at 90% noise level.