Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.

Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

Abstract: A novel image filter based on type-2 fuzzy logic techniques is proposed for detail-preserving restoration of digital images corrupted by impulse noise. The performance of the proposed filter is evaluated for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise filters from the literature. Experimental results show that the proposed filter exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving thin lines, edges, texture, and other useful information within the image.

A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision

Abstract: In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a backpropagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.

Reactions to environmental noise in an ambient noise context in residential areas

Abstract: Direct reanalyses of over 57 000 interview responses to 35 noise sources in 20 social surveys and reviews of publications for over 12 000 additional responses to 16 noise sources in 13 social surveys show that residents’ reactions to an audible environmental noise (a target noise) are only slightly or not at all reduced by the presence of another noise source (ambient noise) in residential environments. The direct reanalyses account for type of noise source (aircraft, road traffic, railway, impulse noise), type of noise reaction question, type of activity disturbance, quality of noise data, type of regression analysis model (linear, logit, probit), two noise metrics (DNL, LAeq), and ten personal characteristics. Although there is considerable variation from survey to survey, the best direct estimate is that approximately a 20-dB increase in ambient noise exposure (95% confidence interval of 15–50 dB) has no more impact than approximately a 1-dB decrease in target noise exposure. Tabulations of 12 findings...

Hyperspectral Images Denoising via Nonconvex Regularized Low-Rank and Sparse Matrix Decomposition

Abstract: Hyperspectral images (HSIs) are often degraded by a mixture of various types of noise during the imaging process, including Gaussian noise, impulse noise, and stripes Such complex noise could plague the subsequent HSIs processing Generally, most HSI denoising methods formulate sparsity optimization problems with convex norm constraints, which over-penalize large entries of vectors, and may result in a biased solution In this paper, a nonconvex regularized low-rank and sparse matrix decomposition (NonRLRS) method is proposed for HSI denoising, which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes The NonRLRS aims to decompose the degraded HSI, expressed in a matrix form, into low-rank and sparse components with a robust formulation To enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions, a novel nonconvex regularizer named as normalized $\varepsilon $ -penalty, is presented, which can adaptively shrink each entry In addition, an effective algorithm based on the majorization minimization (MM) is developed to solve the resulting nonconvex optimization problem Specifically, the MM algorithm first substitutes the nonconvex objective function with the surrogate upper-bound in each iteration, and then minimizes the constructed surrogate function, which enables the nonconvex problem to be solved in the framework of reweighted technique Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method

Method and system for detecting, timing, and correcting impulse noise

Abstract: A system for detecting and correcting impulse noise present on an input data signal includes an impulse detector module receiving an input data signal and producing as output an correction enable signal indicating when an impulse correction is required. An impulse corrector module receives the input data signal and a correction enable signal and produces a corrected data signal, e.g., having the impulse canceled or blanked, as output. A reliability estimator and selector module receives the corrected data signal and the input data signal and selects as output the input signal which is more reliable. In one embodiment, the impulse detector includes first and second complementary impulse detectors, the outputs of which are analyzed by an enable and correction module to produce an impulse detection signal with improved accuracy. Preferably, the enable and correction module also indicates the most appropriate type of impulse correction in accordance with the detection signals from the complementary detectors. A novel system and method of detecting impulses based on Gram Schmidt techniques is also presented. In this method, one or more channels of a multi-channel data signal are kept free of data. When a whitening filter is applied, impulses on these quiet channels are emphasized. The Gram Schmidt technique exploits this fact to provide for improved impulse detection. The system can be modified to detect other types of low dimensionality noise.