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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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Proceedings ArticleDOI
02 Nov 1997
TL;DR: A technique for automatically estimating the noise floor spectrum in the presence of signals based on applying morphological binary image processing operators to a binary image of the received power spectrum, related to rank-order filters but more computationally efficient.
Abstract: This paper describes a technique for automatically estimating the noise floor spectrum in the presence of signals. The technique works equally well for both flat and non-flat noise floor spectra. The technique is based on applying morphological binary image processing operators to a binary image of the received power spectrum. It is related to rank-order filters but is more computationally efficient. The performance is illustrated on the detection of radio signals.

37 citations

Journal ArticleDOI
TL;DR: The proposed recursive spline interpolation filter is based on the neighborhood noise- free pixels and previous noise-free output pixel; hence, it is termed as recursive splines interpolationfilter.
Abstract: Spline-based approach is proposed to remove very high density salt-and-pepper noise in grayscale and color images. The algorithm consists of two stages, the first stage detects whether the pixel is noisy or noise-free. The second stage removes the noisy pixel by recursive spline interpolation filter. The proposed recursive spline interpolation filter is based on the neighborhood noise-free pixels and previous noise-free output pixel; hence, it is termed as recursive spline interpolation filter. The performance of the proposed algorithm is compared with the existing algorithms like standard median filter, decision-based filter, progressive switched median filter, and modified decision-based unsymmetric trimmed median filter at very high noise density. The proposed algorithm gives better peak signal-to-noise ratio, image enhancement factor, and correlation factor results than the existing algorithms.

37 citations

Journal ArticleDOI
TL;DR: In this paper, morphological operations and median filter were used first to remove noise from the original image during pre-processing and the combined Spline and B-spline method was subsequently adopted to enhance the image before segmentation.
Abstract: In a computerized image analysis environment, the irregularity of a lesion border has been used to differentiate between malignant melanoma and other pigmented skin lesions. The accuracy of the automated lesion border detection is a significant step towards accurate classification at a later stage. In this paper, we propose the use of a combined Spline and B-spline in order to enhance the quality of dermoscopic images before segmentation. In this paper, morphological operations and median filter were used first to remove noise from the original image during pre-processing. Then we proceeded to adjust image RGB values to the optimal color channel (green channel). The combined Spline and B-spline method was subsequently adopted to enhance the image before segmentation. The lesion segmentation was completed based on threshold value empirically obtained using the optimal color channel. Finally, morphological operations were utilized to merge the smaller regions with the main lesion region. Improvement on the average segmentation accuracy was observed in the experimental results conducted on 70 dermoscopic images. The average accuracy of segmentation achieved in this paper was 97.21 % (where, the average sensitivity and specificity were 94 % and 98.05 % respectively).

37 citations

Proceedings ArticleDOI
01 Oct 2010
TL;DR: The proposed median non-local means filter for denoising low signal-to-noise-ratio images outperforms the standard Gaussian filtering approach, anisotropic-median diffusion filtering (AMDF) and NLM in terms of visual assessment and trade-off between lesion contrast and noise.
Abstract: Denoising low signal-to-noise-ratio (SNR) images is a significant challenge since the intensity gradient due to noise elements may compete with or even exceed the intensity gradient due to features in the images. This situation can often be encountered in photon-limited medical imaging applications such as MLEM reconstructed Positron Emission Tomography (PET) images. In this study, we propose a median non-local means filter for denoising low-SNR images. The proposed method incorporates a median filtering operation indirectly in the non­local means (NLM) method, which gives more robust estimation of the weights used to average the pixels in the image. For the application of multi-modality imaging such as PET/CT, we further extended the method to incorporate anatomical side information which can be obtained from co-registered CT images without segmentation to preserve abrupt changes between organs on PET images and reduce the computational cost of weight calculations. We applied the proposed method (AMNLM) to a PET/CT simulation, a real physical phantom study and a clinical patient study with lung lesions. The results suggest that the proposed method outperforms the standard Gaussian filtering approach, anisotropic-median diffusion filtering (AMDF) and NLM in terms of visual assessment and trade-off between lesion contrast and noise.

37 citations

Journal ArticleDOI
TL;DR: In this article, a robust f -x projection filtering scheme for simultaneous erratic noise and Gaussian random noise attenuation is proposed, where the estimation of the prediction error filter and the additive noise sequence are performed in an alternating fashion.
Abstract: Linear prediction filters are an effective tool for reducing random noise from seismic records. Unfortunately, the ability of prediction filters to enhance seismic records deteriorates when the data are contaminated by erratic noise. Erratic noise in this article designates non-Gaussian noise that consists of large isolated events with known or unknown distribution. We propose a robust f -x projection filtering scheme for simultaneous erratic noise and Gaussian random noise attenuation. Instead of adopting the 2-norm, as commonly used in the conventional design of f -x filters, we utilize the hybrid 1/2-norm to penalize the energy of the additive noise. The estimation of the prediction error filter and the additive noise sequence are performed in an alternating fashion. First, the additive noise sequence is fixed, and the prediction error filter is estimated via the least-squares solution of a system of linear equations. Then, the prediction error filter is fixed, and the additive noise sequence is estimated through a cost function containing a hybrid 1/2-norm that prevents erratic noise to influence the final solution. In other words, we proposed and designed a robust M-estimate of a special autoregressive moving-average model in the f -x domain. Synthetic and field data examples are used to evaluate the performance of the proposed algorithm.

37 citations


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