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Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.


Papers
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Journal ArticleDOI
TL;DR: The fingertip OCT images indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI).
Abstract: Non-local means (NLM) filter is one of the state-of-the-art denoising filters. It exploits the presence of similar features in an image and averages those similar features to remove noise. However, a conventional NLM filter shows somewhat inferior performance of noise reduction around edges, suffering from low efficiency of collecting similar features to be averaged. In order to overcome this phenomenon, we propose a NLM filter with double Gaussian anisotropic kernels as a substitute for the conventional homogeneous kernel to effectively remove noise from OCT images corrupted by speckle noise. The proposed filter was evaluated by comparing with various denoising filters such as conventional NLM filter, median filter, bilateral filter, and Wiener filter. The fingertip OCT images, which were processed with the different denoising filters, indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI). A human retina OCT image was also used to compare and show the performances of noise reduction among different filters. In addition, the denoising performance with the proposed NLM filter was also investigated in the synthetic images for fair comparison among the filters by calculating the peak signal-to-noise ratio (PSNR). The proposed NLM filter outperformed the conventional NLM filter as well as the other filters.

78 citations

Journal ArticleDOI
TL;DR: A novel approach to impulsive noise detection in color images is introduced and a switching filter between the arithmetic mean filter (AMF) and the identity operation is proposed, which achieves a trade-off between noise suppression and signal-detail preservation.

78 citations

Journal ArticleDOI
TL;DR: Digital computer simulations of grain noise suppression using two particular cases of this additive, "signal-modulated" noise model were performed, demonstrating the potential advantages of noise suppression filters which make use of a priori knowledge of the signal-dependent nature of the grain noise.
Abstract: Image detection noise is a fundamental limitation in picture processing, whether analog or digital. This noise is characteristically signal-dependent and this signal-dependence introduces significant problems in the design of appropriate noise-suppression techniques. This paper outlines some recent results obtained by the authors in the optimum suppression of two types of signal-dependent image noise: film-grain noise and photoelectron shot noise. The work in grain noise suppression involves deriving the minimum-mean-square error Wiener filter for a new form of signal-dependent noise model suggested in earlier work by T. S. Huang. Implementation of these filters by either coherent optical or digital processing techniques is possible. Digital computer simulations of grain noise suppression using two particular cases of this additive, "signal-modulated" noise model were performed. They demonstrate the potential advantages of noise suppression filters which make use of a priori knowledge of the signal-dependent nature of the grain noise. The results of work on linear, unbiased restoration of images recorded in the presence of photoelectron noise are summarized. Additional work in both of these areas is suggested, with a particular need existing for correlating the properties of various models proposed for grain noise with experimental data obtained on emulsions using scanning microdensitome ters.

78 citations

Journal ArticleDOI
TL;DR: A novel approach for removing noise from multiple reflections based on an adaptive randomized-order empirical mode decomposition (EMD) framework and the EMD-based smoothing method can help preserve the flattened signals better, without the need of exact flattening, and can preserve the amplitude variation much better.
Abstract: We propose a novel approach for removing noise from multiple reflections based on an adaptive randomized-order empirical mode decomposition (EMD) framework. We first flatten the primary reflections in common midpoint gather using the automatically picked normal moveout velocities that correspond to the primary reflections and then randomly permutate all the traces. Next, we remove the spatially distributed random spikes that correspond to the multiple reflections using the EMD-based smoothing approach that is implemented in the $f-x$ domain. The trace randomization approach can make the spatially coherent multiple reflections random along the space direction and can decrease the coherency of near-offset multiple reflections. The EMD-based smoothing method is superior to median filter and prediction error filter in that it can help preserve the flattened signals better, without the need of exact flattening, and can preserve the amplitude variation much better. In addition, EMD is a fully adaptive algorithm and the parameterization for EMD-based smoothing can be very convenient.

77 citations

Journal ArticleDOI
TL;DR: An adaptive median based filter is proposed for removing noise from images that consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.
Abstract: An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.

77 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202372
2022186
2021276
2020387
2019478
2018538