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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TL;DR: An adaptive two-pass rank order filter to remove impulse noise in highly corrupted images by selectively replacing some pixels changed by the first pass of filtering with their original observed pixel values during the second filtering.
Abstract: In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.
77 citations
Patent•
05 Feb 1996TL;DR: In this article, a filtering operation on an image signal obtained by decoding data that has been coded with a unit of a block consisting of m×n pixels is proposed, where a filter circuit having a plurality of filter characteristics suppresses noise, and a characteristics selection circuit switches the filter characteristics of the filter circuit by using a quantizing parameter employed for coding the image signal.
Abstract: Noise contained in a reproducing image signal is suppressed by performing a filtering operation on an image signal obtained by decoding data that has been coded with a unit of a block consisting of m×n pixels. A filter circuit having a plurality of filter characteristics suppresses noise, and a characteristics selection circuit switches the filter characteristics of the filter circuit by use of a quantizing parameter employed for coding the image signal.
77 citations
TL;DR: A heuristic optimization algorithm searches for the contour initialized from a prostate model and combines adaptive morphological filtering and median filtering to detect the noise-containing regions and smooth them in trans-abdominal ultrasound images of the prostate.
77 citations
TL;DR: An adaptive image restoration algorithm based on 2-D bilateral filtering has been proposed to enhance the signal-to-noise ratio (SNR) of the intrusion location for phase-sensitive optical time domain reflectometry system and has the potential to precisely extract intrusion location from a harsh environment with strong background noise.
Abstract: An adaptive image restoration algorithm based on 2-D bilateral filtering has been proposed to enhance the signal-to-noise ratio (SNR) of the intrusion location for phase-sensitive optical time domain reflectometry (Ф-OTDR) system. By converting the spatial and time information of the Ф-OTDR traces into 2-D image, the proposed 2-D bilateral filtering algorithm can smooth the noise and preserve the useful signal efficiently. To simplify the algorithm, a Lorentz spatial function is adopted to replace the original Gaussian function, which has higher practicability. Furthermore, an adaptive parameter setting method is developed according to the relation between the optimal gray level standard deviation and noise standard deviation, which is much faster and more robust for different types of signals. In the experiment, the SNR of location information has been improved over 14 dB without spatial resolution loss for a signal with original SNR of 6.43 dB in 27.6 km sensing fiber. The proposed method has the potential to precisely extract intrusion location from a harsh environment with strong background noise.
77 citations
TL;DR: An Iterative Mean Filter (IMF) is proposed to eliminate the salt-and-pepper noise by using the mean of gray values of noise-free pixels in a fixed-size window and outperforms the other state-of-the-art methods.
Abstract: We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of $3\times3$ . This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).
77 citations