Topic
Median filter
About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.
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Papers
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14 May 2008
TL;DR: The denoising algorithm is a rewriting of the recently proposed nonlocal mean filter that builds on the separable property of neighborhood filtering to offer a fast parallel and vectorized implementation in contemporary shared memory computer architectures while reducing the theoretical computational complexity of the original filter.
Abstract: We present an efficient algorithm for nonlocal image filtering with applications in electron cryomicroscopy. Our denoising algorithm is a rewriting of the recently proposed nonlocal mean filter. It builds on the separable property of neighborhood filtering to offer a fast parallel and vectorized implementation in contemporary shared memory computer architectures while reducing the theoretical computational complexity of the original filter. In practice, our approach is much faster than a serial, non-vectorized implementation and it scales linearly with image size. We demonstrate its efficiency in data sets from Caulobacter crescentus tomograms and a cryoimage containing viruses and provide visual evidences attesting the remarkable quality of the nonlocal means scheme in the context of cryoimaging. With such development we provide biologists with an attractive filtering tool to facilitate their scientific discoveries.
262 citations
TL;DR: The vector median filter (VMF) as discussed by the authors is an extension of the MF, which outputs for each window location a number of data elements, and is obtained as a VMF special case by adjusting the VMF parameters.
260 citations
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TL;DR: This paper presents a complete and quantitative analysis of noise models available in digital images and expresses a brief overview of various noise models.
Abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.
256 citations
TL;DR: Tests were performed on synthetic aperture radar images which show that the algorithm reduces speckle noise in images favorably with a 3 × 3 median filter.
Abstract: An algorithm is described which reduces speckle noise in images. It is a nonlinear algorithm based on geometric concepts. Tests were performed on synthetic aperture radar images which show that it compares favorably with a 3 × 3 median filter.
248 citations
TL;DR: A new method for impulse noise removal is presented, where a robust estimator of the variance, MAD (median of the absolute deviations from the median), is modified and used to efficiently separate noisy pixels from the image details.
Abstract: A new method for impulse noise removal is presented, where a robust estimator of the variance, MAD (median of the absolute deviations from the median), is modified and used to efficiently separate noisy pixels from the image details. The algorithm is free of varying parameters, requires no previous training or optimization, and successfully removes all types of impulse noise. The pixel-wise MAD concept is straightforward, low in complexity, and achieves high filtering performance.
246 citations