Topic
Median filter
About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.
Papers published on a yearly basis
Papers
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TL;DR: The universal quality index, proposed in this paper to measure the effectiveness of denoising, suggests that the anisotropic median-diffusion filter can retain adherence to the original image intensities and contrasts better than other filters.
Abstract: We propose a new anisotropic diffusion filter for denoising low-signal-to-noise molecular images. This filter, which incorporates a median filter into the diffusion steps, is called an anisotropic median-diffusion filter. This hybrid filter achieved much better noise suppression with minimum edge blurring compared with the original anisotropic diffusion filter when it was tested on an image created based on a molecular image model. The universal quality index, proposed in this paper to measure the effectiveness of denoising, suggests that the anisotropic median-diffusion filter can retain adherence to the original image intensities and contrasts better than other filters. In addition, the performance of the filter is less sensitive to the selection of the image gradient threshold during diffusion, thus making automatic image denoising easier than with the original anisotropic diffusion filter. The anisotropic median-diffusion filter also achieved good denoising results on a piecewise-smooth natural image and real Raman molecular images.
106 citations
Proceedings Article•
01 Nov 2012TL;DR: A general method for image contrast enhancement and noise reduction is proposed, developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious.
Abstract: A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the dark areas become bright and the contrast get enhanced. Then, the joint-bilateral filter with the original green component as the edge image is introduced to suppress the noise. Experimental results validate the performance of the proposed approach.
106 citations
TL;DR: A large class of physical phenomena observed in practice exhibit non-Gaussian behavior, and the /spl alpha/-stable distributions, which have heavier tails than Gaussian distributions, are considered to model non- Gaussian signals.
Abstract: A large class of physical phenomena observed in practice exhibit non-Gaussian behavior. In the letter /spl alpha/-stable distributions, which have heavier tails than Gaussian distributions, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such a noise is a requirement of many practical problems. Since direct application of commonly used adaptation techniques fail in these applications, new algorithms for adaptive filtering for /spl alpha/-stable random processes are introduced. >
106 citations
03 Apr 2008
TL;DR: A new algorithm, the spatial median filter, is introduced and compared with current image smoothing techniques, demonstrating that the proposed algorithm is comparable to these techniques.
Abstract: In this paper, six different image filtering algorithms are compared based on their ability to reconstruct noise-affected images The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image A new algorithm, the spatial median filter, is introduced and compared with current image smoothing techniques Experimental results demonstrate that the proposed algorithm is comparable to these techniques A modification to this algorithm is introduced to achieve more accurate reconstructions over other popular techniques
106 citations
TL;DR: A new implementation of the Wiener restoration filter that uses as its optimality criterion the minimization of the mean-square error between the undistorted image of the object and the filtered image to improve the quality of digital nuclear medicine images.
Abstract: To improve the quality of digital nuclear medicine images, we have developed a new implementation of the Wiener restoration filter The Wiener filter uses as its optimality criterion the minimization of the mean‐square error between the undistorted image of the object and the filtered image In order to form this filter, the object and noise power spectrums are needed The noise power spectrum for the count‐dependent Poisson noise of nuclear medicine images is shown to have a constant average magnitude equal to the total count in the image The object power spectrum is taken to be the image power spectrum minus the total count, except in the noise dominated region of the image power spectrum where a least‐squares‐fitted exponential is used Processing time is kept to a clinically acceptable time frame through use of an array processor Pronounced noise suppression and detail enhancement are noted with use of this filter with clinical images
105 citations