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: In this paper, three operating modes: averaging, recursive filtering and Kalman filtering have been investigated, and it is shown that there is an optimum choice of filter parameters for each of them.
Abstract: SUMMARY
Digital framestores can be used to reduce noise in TV rate electron microscope images. Three operating modes: averaging, recursive filtering and Kalman filtering have been investigated. It is shown that there is an optimum choice of filter parameters.
53 citations
TL;DR: Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
Abstract: Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
53 citations
TL;DR: It is shown that the direct moment calculation combined with a consensus averaging technique has the best overall performance for accuracy and the ability to use data with a very low signal-to-noise ratio.
Abstract: A numerical model to simulate radar data is used for testing various estimators of the Doppler shift in Doppler radar echoes. The estimators are the pulse pair and poly-pulse pair algorithms in the correlation domain, a least-squares fitting to the spectral peak of the power spectra, and direct calculations of the moments from periodograms in the spectral domain. Two averaging schemes (a consensus average and a median filter) are also examined for data with poor signal-to-noise ratios. The data processing method used in Doppler radar wind profilers, which operate over a very wide range of signal to noise ratios, is examined in detail. It is shown that the direct moment calculation combined with a consensus averaging technique has the best overall performance for accuracy and the ability to use data with a very low signal-to-noise ratio.
53 citations
TL;DR: Using threshold decomposition, the root structure of the recursive separable median filter is derived, where a root is a signal invariant to further filtering, and it is shown that these root structures differ from those of their nonrecursive counterparts.
Abstract: The recursive separable median filter has been successfully used to extract features from noisy two-dimensional signals. In many applications, it gives better noise suppression and edge preservation than the standard separable median filter. In this paper we use a new approach for studying the deterministic properties of separable median filters. In particular, using threshold decomposition, we derive the root structure of the recursive separable median filter, where a root is a signal invariant to further filtering. It is shown that these root structures differ from those of their nonrecursive counterparts. We also show that any two-dimensional signal will converge to a root after repeated passes of the recursive separable median filter.
53 citations
TL;DR: A simple explicit image filter which can filter out noise while preserving edges and fine-scale details is derived, which has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks.
Abstract: In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.
53 citations