Multilevel median filters for image processing

2002 ◽  
Author(s):  
R. Wichman ◽  
Y. Neuvo
Keyword(s):  
Image Processing ◽  
Median Filters

Application of Combined Median- Averaging Filters to Scintigraphic Image Processing

1985 ◽  
Vol 24 (04) ◽  
pp. 164-168 ◽  
Author(s):  
P. Mitraszewski ◽  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Image Processing ◽  
High Frequency ◽  
Impulse Noise ◽  
Simple Algorithm ◽  
Frequency Noise ◽  
Median Filtering ◽  
Promising Candidate ◽  
High Frequency Noise ◽  
Median Filters ◽  
Averaging Filter

SummaryStatistical and deterministic properties of median filters are briefly discussed and their inherent advantages as a prospective tool in scintigraphic data processing are pointed out. The ability of median filters of suppressing impulse noise while the edge-like features of an image are preserved, is demonstrated on phantom data. The residual high-frequency noise remaining after median filtering can be subsequently reduced by standard smoothing procedures. A simple algorithm, made up of the superposition of a median and an averaging filter, is presented and shown to be a promising candidate in the quest for fast and easy-to-implement processing routine.

scholarly journals High Salt and Pepper Noise Ratio Reduction

2021 ◽  
Vol 10 (9) ◽  
pp. 88-97
Author(s):  
Hatim Zaini ◽  
Ziad Alqadi
Keyword(s):  
Image Processing ◽  
Noise Level ◽  
Digital Images ◽  
Salt And Pepper Noise ◽  
High Noise ◽  
Median Filters ◽  
Negative Effect ◽  
Noise Ratio ◽  
Blurred Images ◽  
Salt And Pepper

Colored digital images are affected by salt and pepper noise, affecting their clarity, contents and properties. The negative effect on the image increases with the increase in the noise level. Filters based on average and median filters are not able to remove SAPN with high noise ratios, and accordingly, blurred images are obtained that cannot be dealt with in various image processing operations. In this paper research a modification will be add to median and average filters making them capable of reducing the noise even it has a high noise ratio, the modified average and median filters will be implanted and some comparisons with other popular filters will be made to show the enhancement of the modified filters.

scholarly journals Multivariate Medians for Image and Shape Analysis

2019 ◽  
Author(s):  
Martin Welk
Keyword(s):  
Image Processing ◽  
Shape Analysis ◽  
Median Filters ◽  
Geometric Data ◽  
Multivariate Median ◽  
Shape Processing ◽  
Multivariate Images ◽  
Physical Quantities

Having been studied since long by statisticians, multivariate medianconcepts found their way into the image processing literature in thecourse of the last decades, being used to construct robust and efficientdenoising filters for multivariate images such as colour images but alsomatrix-valued images.Based on the similarities between image and geometric data as results ofthe sampling of continuous physical quantities, it can be expected that theunderstanding of multivariate median filters for images provides a startingpoint for the development of shape processing techniques.This paper presents an overview of multivariate median concepts relevantfor image and shape processing. It focusses on their mathematical principlesand discusses important properties especially in the context of imageprocessing.

scholarly journals CONSTANT TIME SCANNING AND BETTER EDGE PRESERVATION FOR BETTER PERFORMING AND QUALITY OF MEDIAN FILTER

2012 ◽  
pp. 114-116
Author(s):  
RITWIK SHARMA ◽  
SHUBHAM HARNAL
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Median Filtering ◽  
Edge Preservation ◽  
Median Filters ◽  
Novel Approach ◽  
Time Scanning ◽  
The Cost ◽  
Salt And Pepper

The median filter is an important filter in many image processing algorithms and especially in removal of salt and pepper noise. Traditional median filters either focus on improving the performance or the quality of the median filtering. Generally, the methods which optimize performance do so at the cost of quality and vice-versa. In this paper a novel approach to median filtering is presented providing both better performance and quality without sacrificing either. The analysis is presented with respect to image processing and the results obtained are presented in tabular form.

scholarly journals Image Edge Detection Algorithm Based on Fuzzy Radial Basis Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Feng ◽  
Jian Wang ◽  
Chao Ding
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Median Filters ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Radial Basis ◽  
Image Edge

Digital image processing technology is widely used in production and life, and digital images play a pivotal role in the ever-changing technological development. Noise can affect the expression of image information. The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. Therefore, suppressing noise and improving the accuracy of edge detection are important aspects of image processing. To address these issues, this paper presents a new detection algorithm combined with information fusion based on the existing image edge detection techniques, and the algorithm is studied from two aspects of fuzzy radial basis fusion discrimination, in terms of preprocessing algorithm, comparing the denoising effect of mean and median filters with different template sizes on paper images with added noise, and selecting the improved median filter denoising, comparing different operator edge detection. The effect of image edge detection contour is finally selected as the 3 ∗ 3 Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle and labeled, and then, the original paper image is scanned line by line to segment the target image edge region. The image edge detection algorithm based on fuzzy radial basis fuser can not only speed up the image preprocessing, meet the real-time detection, and reduce the amount of data processed by the upper computer but also can accurately identify five image edge problems including folds and cracks, which has good application prospects.

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