scholarly journals Adaptive algorithm for reducing pulse noise level in images from CCTV cameras

2021 ◽  
pp. 21-27
Author(s):  
А. V. Sadchenko ◽  
O. A. Kushnirenko ◽  
A. V. Troyanskiy ◽  
Yu. A. Savchuk
Keyword(s):  
Impulse Noise ◽  
Reconstructed Image ◽  
Correct Identification ◽  
Median Filtering ◽  
Noise Variance ◽  
Discrete Elements ◽  
Noise Levels ◽  
Objective Criterion ◽  
Median Filters ◽  
Filtering Algorithm

An optical signal is usually converted into an electrical one by using photosensitive matrices with a large number of discrete elements based on charge-coupled device (CCD) technology or CMOS technology. One of the disadvantages of CCD and CMOS technologies is the impulse conversion noise that appears on digitized images, impairing visual perception and significantly reducing the likelihood of correct identification in pattern recognition tasks. Traditionally, impulse noise is removed from images using median filters with a fixed aperture within each iteration of full-format processing. However, such filters reduce the sharpness of the reconstructed image at high noise levels or insufficiently suppress the interference under the same noise conditions. These setbacks call for a need to develop an adaptive median filtering algorithm, which would produce a reconstructed image as a joint result of processing with median filters with different apertures. The essence of this algorithm is to select image areas with different noise levels and process these areas with filters with different apertures. As an objective criterion for assessing the efficiency of the proposed filtering algorithm, the authors used the criterion of the maximum correlation coefficient between noise-free and non-noisy images at various values of the noise variance. The mathematical modeling performed in this study allowed finding that with an increase in the impulse noise variance, the gain of the adaptive median filtering algorithm increases exponentially, in comparison with the algorithms using the filters with a fixed aperture value. The proposed algorithm can be used for pre-preprocessing images intended for recognition by machine vision systems, scanning text, and improving subjective image characteristics, such as sharpness and contrast.

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 A Biased Median Filtering Algorithm for Segmentation of Intestinal Cell Gland Images

2006 ◽  
Vol 6 ◽  
pp. 200-220 ◽  
Author(s):  
Hai-Shan Wu ◽  
Joan Gil
Keyword(s):  
Median Filter ◽  
Goblet Cells ◽  
Segmentation Algorithm ◽  
Intestinal Cell ◽  
Median Filtering ◽  
Median Filters ◽  
Filtering Algorithm ◽  
Identical Dimension ◽  
Intestine Cell ◽  
Image Segmentation Algorithm

In this paper, we introduce a biased median filtering image segmentation algorithm for intestinal cell glands consisting of goblet cells. While segmentation of individual cells are generally based on the dissimilarities in intensities, textures, and shapes between cell regions and background, the proposed segmentation algorithm of intestine cell glands is based on the differences in cell distributions. The intestine cell glands consist of goblet cells that are distributed in the chain-organized patterns in contrast to the more randomly distributed nongoblet cells scattered in the bright background. Four biased median filters with long rectangular windows of identical dimension, but different orientations, are designed based on the shapes and distributions of cells. Each biased median filter identifies a part of gland segments in a particular direction. The complete gland regions are the combined responses of the four biased median filters. A postprocessing procedure is designed to reduce the defects that may occur when glands are located very close together and to narrow the gapping areas because of the thin distribution of goblet cells. Segmentation results of real intestinal cell gland images are provided to show the effectiveness of the proposed algorithm.

Application of Improved Median Filtering Algorithm to Image De-Noising

2014 ◽  
Vol 998-999 ◽  
pp. 838-841
Author(s):  
Wen Long Jiang ◽  
Guang Lin Li ◽  
Wei Bing Luo
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Algorithm ◽  
Weighted Median ◽  
The Mean ◽  
Weighted Median Filter ◽  
Mean Filtering

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

Impulse Noise Filtering Algorithm Based on Dual Threshold Criterion

2013 ◽  
Vol 437 ◽  
pp. 849-852 ◽  
Author(s):  
Yu Lan Wei ◽  
Bing Li ◽  
Jian Dong Li ◽  
Ying Ying Fan
Keyword(s):  
Impulse Noise ◽  
Surface Defect ◽  
Median Filter ◽  
Noise Filtering ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Obvious Advantage ◽  
Noise Point ◽  
Threshold Criterion ◽  
Good For

In order to filter the impulse noise existing in the weld surface defect images, the traditional median filtering will dim image, even destroy some details in the image, We put forward a new filtering algorithm based on dual threshold Criterion. This way distinguishes the noise point and signal location in the first, and it only doing median filter to the noise point. Lastly, it solves the image’s boundary. We can find it when compared to the traditional median filtering and modified extremum median filtering, the way in this article can be good for filtration, and can keep the image’s details, which have obvious advantage.

scholarly journals Design of Decision Based Recursive Weighted Median Filter With Exponential Weights

2021 ◽  
Vol 2089 (1) ◽  
pp. 012016
Author(s):  
Motepalli Siva Rama Ganesh ◽  
Kalyan Sagar Kadali ◽  
Ramu Bhukya ◽  
Y.T.R. Palleswari ◽  
Asapu Siva ◽  
...  
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Mean Square ◽  
Noise Levels ◽  
Exponential Weights ◽  
High Noise ◽  
Median Filters ◽  
Weighted Median ◽  
Weighted Median Filter ◽  
Image Details

Abstract The prescribed algorithm for removing impulse noise effectively even under high noise densities without causing any loss of image details. Hence a cascaded section of median filters that, involves an Decision-based Median Filter followed by a Recursive Weighted Median (RWM) Filter employing exponential weights are used. The median controlled algorithm is employed to calculate the exponential weights. In the algorithms that where proposed in earlier which involves a cascaded section of the median with the RWM filters provided lesser Peak Signal/Noise Ratio (PSNR) and greater Mean Square Error(MSE) values. Hence the output appeared to be distorted for higher noise levels. These drawbacks have been eliminated in this proposed algorithm.

Preserving Median Filtering Algorithm in Chip Images

2014 ◽  
Vol 8 (1) ◽  
pp. 460-466
Author(s):  
Ding Ming
Keyword(s):  
Noise Level ◽  
Impulse Noise ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Shape And Size

The types and characteristics of noise in chip images were analyzed in this paper. A new kind of details preserving adaptive median filtering algorithm with directional sub-window was introduced in which the shape and size of filtering window can be adjusted according to the noise level and the sensitivity can be set by parameter. The filter can effectively get rid of most of the impulse noise and speckles from the chip image, and improve the accuracy of processing and analyzing image.

Filtering Algorithm for Removing Image Impulse Noise

2014 ◽  
Vol 543-547 ◽  
pp. 2817-2820
Author(s):  
Li Hua Sun ◽  
En Liang Zhao ◽  
Bao Yang Yu
Keyword(s):  
Numerical Experiments ◽  
Small Area ◽  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Effective Algorithm ◽  
Signal To Noise ◽  
Edge Information ◽  
Filtering Algorithm ◽  
Pixel Value

The paper is devoted to put forward an effective algorithm for removing image impulse noise by improving the traditional median filter. The following approaches are developed in this paper. First, in a small area all the pixels are separated into signal pixels and suspicious noise pixels making use of the extremum and median filtering algorithm .Then suspicious noise pixels are further identified the signal pixels and noise pixels around pixels. If a pixel value is a noise pixel, it will be processed by median filter. A signal pixel will retain the original pixel value. The numerical experiments show that compared with traditional median filter , the details and edge information of the original image are retained when the new algorithm is used , Comparing with extremum median filter algorithm, the ratio of peak signal to noise can increase up to 2.94 at most. As a result, it is an effective algorithm for image denoising that can eliminate impulse noise in the image effectively.

Adaptive median filtering algorithm based on slope

2013 ◽  
Vol 32 (3) ◽  
pp. 736-738
Author(s):  
Shu-juan LIU ◽  
Ye ZHAO ◽  
Rui DONG ◽  
Zhi-wei WANG ◽  
Fang-fang YANG
Keyword(s):  
Median Filtering ◽  
Filtering Algorithm
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