Topic
Impulse noise
About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.
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TL;DR: A locally adaptive regularized super-resolution model for images with mixed noise and outliers adaptively assigns the local norms in the data fidelity term of the regularized model according to the impulse noise and motion outlier detection results.
61 citations
TL;DR: A robust Bayesian compressed sensing framework is introduced to account for sign flip errors and a variational expectation-maximization algorithm is developed to identify the sign-flip errors and recover the sparse signal simultaneously.
Abstract: We consider the problem of sparse signal recovery from one-bit measurements Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states These bit-flip errors, also referred to as the sign-flip errors, may result in severe performance degradation To address this issue, we introduce a robust Bayesian compressed sensing framework to account for sign flip errors Specifically, sign-flip errors are considered as a result of a sparse noise-corrupted model in which original (unquantized) observations are corrupted by sparse (impulse) noise A Gaussian-inverse Gamma hierarchical prior is assigned to the noise vector to promote sparsity Based on the modified hierarchical model, we develop a variational expectation-maximization (EM) algorithm to identify the sign-flip errors and recover the sparse signal simultaneously Numerical results are provided to illustrate the effectiveness and superiority of the proposed method
60 citations
59 citations
TL;DR: This letter proposes a new technique of restoring images distorted by random-valued impulse noise, based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window.
Abstract: This letter proposes a new technique of restoring images distorted by random-valued impulse noise. The detection process is based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window. The tested pixel is deemed original if it is similar to the pixels in the optimum direction. Extensive simulations prove that the proposed technique has superior performance, when compared to other existing methods, especially at high noise rates.
59 citations
TL;DR: A new efficient approach to detect the impulse noise from the corrupted image using feed forward neural network (FFNN) using a modified version of the arithmetic mean filter to remove the detected impulse noise.
59 citations