Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Method and apparatus for processing images

Abstract: A method and apparatus for reducing noise and enhancing the dynamic range of an image data set gather from an array (12) of transducers (T). The method includes the step of processing the image data set in a digital computer (17) by a noise reduction technique, such as deconvolving the noise component by means of a CLEAN or other algorithm. Thereafter the artifact image data introduced by the noise reduction technique is reduced by masking the processed image data set with the original image data set. This masking is done by multiplying each data value in the processed image data set by the corresponding data value in the original image data set. The method further includes scaling and normalizing the masked data and finally displaying the same on an image display device (42). Additionally, for imaging apparatus (10) not having a cross-correlator (16), phase aberration is reduced by performing a coordinate transformation step prior to noise reduction using a non-standard set of coordinate transformation algorithms.

Efficiency analysis of color image filtering

Abstract: This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

A New Filtering Algorithm Based on Extremum and Median Value

Abstract: A new median based filtering algorithm-extremum median filtering is presented In order not to perturb the efficient signals as much as possible when the noises are removed, the following approaches are developed in this paper First, all the pixels are separated into signal pixels and noise pixels according to the decision criterion given in the following; then, noise pixels are replaced with the median value of their neighborhood in the input image The decision criterion: if a pixel value is the extremum (max or min) of its neighborhood, it is a noise pixel; else, it is a signal pixel This decision criterion is under such an assumption: inherent relationships exist among neighbor pixels If a pixel value is far higher or lower than the others' value of its neighborhood are, that is to say, a pixel has lower correlation with its neighbors, we may consider that it had been contaminated with noise Else, if it is similar to the others, we consider that it represents an effective signal Experimental results show that the assumption fits the facts quit wellIn this paper, attention is forcused on filtering of images degraded by "salt and pepper" noises Examples on images containing 184×148 pixels are givenExperimental results show that the EM filtering has better performance than standard median filtering with less subtle details being eliminated The SNR of the image filtered with EM filter is about 4dB higher than that with median filter This is because the operation only affects noise pixels and most of the uncontaminated pixels keep intact Especially,in the case of lower SNR,larger filtering window improves the SNR notably Median filter is not the case, for the filtering operation blurs the image extremely with the increasing of the filtering window

Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms

Abstract: This paper deals with the problem of identifying the nature of noise and estimating its standard deviation from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. In this study, we focus our attention on three classes of degraded noise images, the first one being degraded by an additive noise, the second one by a multiplicative noise and the latter by an impulsive noise. First, in order to identify the nature of the noise, we propose a new approach consisting of characterizing each class by a parameter obtained from histograms computed on several homogeneous regions of the observed image. The homogeneous regions are obtained by segmenting images. Then, the estimation of the standard deviation is achieved from the analysis of an histogram of local standard deviations computed on each of the homogeneous regions.