This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected ?noise pixels? will then be subjected to the second stage of the filtering action, while ?noise-free pixels? are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.

Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

Under the framework of switching median filtering, a highly effective algorithm for impulse noise detection is proposed aiming at providing solid basis for subsequent filtering. This algorithm consists of two iterations to make the decision as accurate as possible. Two robust and reliable decision criteria are proposed for each iteration. Extensive simulation results show that the false alarm rate and miss detection rate of the proposed algorithm are both very low and substantially outperform existing state-of-the-art algorithms. At the same time, the proposed algorithm is in principle simpler as it is intuitive and it is easy to implement as it has uncomplicated structure and few codes.

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A Highly Effective Impulse Noise Detection Algorithm for Switching Median Filters

Image fusion schemes play a vital role in medical image analysis and treatment planning. In this paper, a novel principal component averaging fusion based on discrete wavelet transform is proposed for fusion of CT-MRI and MRI images. Even though multiscale fusion methods result in effective integration of image details; Pixel level fusion methods based on principal component analysis (PCA) schemes do not loose popularity because of conceptual simplicity. Conventional principal components analysis fusion evaluates principal components based on eigen values of the source images. Using discrete wavelet transform (DWT), source images are decomposed into multiscale inputs and the principal components are evaluated for multiscale coefficients. Average of principal components of all these relevant decomposed elements will constitute weights for fusion rule. This method incorporates the advantage of wavelet transform into PCA fusion in the form of eigen values of multiscale representations. Performance of the proposed method is experimented on CT-MRI and MRI medical images. Analysis of qualitative and quantitative metrics clearly demonstrates that this method exhibits superior results than many other well known fusion schemes.

Discrete wavelet transform based principal component averaging fusion for medical images

A comprehensive survey on impulse and Gaussian denoising filters for digital images

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.

Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression

A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images. First, the directional difference based noise detector is used to identify the noisy pixels by comparing the minimum absolute value of four mean differences between the current pixel and its neighbors in four directional windows with a predefined threshold. Then, the adaptive weighted mean filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window. Numerous simulations demonstrate that the proposed filter outperforms many other existing algorithms in terms of effectiveness in noise detection, image restoration and computational efficiency.

Xuming Zhang

Papers

Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter