Noise removal in three-fringe photoelasticity by median filtering

2009 ◽  
Vol 47 (11) ◽  
pp. 1226-1230 ◽  
Author(s):  
Venketesh N. Dubey ◽  
Gurtej S. Grewal
Keyword(s):  
Noise Removal ◽  
Median Filtering

scholarly journals A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

2020 ◽  
Vol 9 (4) ◽  
pp. 1305-1309
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Domain ◽  
Median Filtering ◽  
Impulse Noise Removal ◽  
Noise Density ◽  
Improved Accuracy ◽  
Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

scholarly journals Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm

2021 ◽  
Vol 3 (4) ◽  
pp. 284-297
Author(s):  
B. Vivekanandam
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Common Type ◽  
Imaging Systems ◽  
Research Report ◽  
Median Filtering ◽  
Challenging Problem ◽  
Sensor Devices ◽  
Original Picture ◽  
Filtering Approach

Thermal noise is the most common type of contamination in digital image acquisition operations, and is caused by the temperature condition of the industrial sensor devices used in the process. When it comes to picture improvement, removing noise from the image is one of the most crucial steps. However, in image processing, it is more critical to retain the characteristics of the original picture while eliminating the noise. Thermal noise removal is a challenging problem in image denoising. This article provides a strategy based on a Hybrid Adaptive Median (HAM) filtering approach for removing thermal noise from the image output of an industrial sensor. The demonstration of this proposed approach's ability, is to successfully detect and reduce thermal noise. In addition, this study examines an adaptive hybrid adaptive median filtering approach that has significant computational advantages, making it highly practical. Finally, this research report on experiments shows the high-quality industrial sensor imaging systems that have been successfully implemented in the real world.

scholarly journals A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song ◽  
Jinsong Tao
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Diffusion Method ◽  
Median Filtering ◽  
Noise Characteristics ◽  
Weighted Total Variation ◽  
Salt And Pepper

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.

A Median Filtering Algorithm Based on Extremum Detection

2014 ◽  
Vol 889-890 ◽  
pp. 1099-1102 ◽  
Author(s):  
Liang Yan ◽  
Gao Yan ◽  
Qin Xia Zhao
Keyword(s):  
Recovery Process ◽  
Noise Removal ◽  
Experimental Results ◽  
Noise Filtering ◽  
Median Filtering ◽  
Noise Detection ◽  
False Detection ◽  
Filtering Method ◽  
Filtering Algorithm ◽  
Two Stages

In this paper a improved algorithm of median filtering based on extremum detection is introduced. According to the deficiencies of the extreme median filtering method in the two stages of noise detection and noise filtering, Adds a false detection noise pixel gray value correction recovery process. Experimental results show that the algorithm in this paper are superior than traditional median filtering algorithm and some improved image filtering algorithm in noise removal and edge retention.

scholarly journals A New Idea of Fast Three-Dimensional Median Filtering for Despeckling of Optical Coherence Tomography Images

2015 ◽  
Vol 20 (3) ◽  
pp. 25-34 ◽  
Author(s):  
Jarosław Gocławski ◽  
Joanna Sekulska-Nalewajko
Keyword(s):  
Optical Coherence Tomography ◽  
Graphics Processing Unit ◽  
Median Filter ◽  
Three Dimensional ◽  
Speckle Noise ◽  
Noise Removal ◽  
Processing Unit ◽  
Optical Coherence ◽  
Median Filtering ◽  
Local Intensity

Abstract Median filtering has been widely used in image processing for noise removal because it can significantly reduce the power of noise while limiting edge blurring. This filtering is still a challenging task in the case of three-dimensional images containing up to a billion of voxels, especially for large size filtering windows. The authors encountered the problem when applying median filter to speckle noise reduction in optical coherence tomography images acquired by the Spark OCT systems. In the paper a new approach to the GPU (Graphics Processing Unit) based median smoothing has been proposed, which uses two-step evaluation of local intensity histograms stored in the shared memory of a graphic device. The solution is able to output about 50 million voxels per second while processing the neighbourhood of 125 voxels by Quadro K6000 graphic card configured on the Kepler architecture.

Uncorrelated Weighted Median Filtering for Noise Removal in SuperDARN

2014 ◽  
Author(s):  
Clarence Goh ◽  
John C. Devlin ◽  
Dennis Deng ◽  
Andrew McDonald ◽  
Muhammad Ramlee Kamarudin
Keyword(s):  
Noise Removal ◽  
Median Filtering ◽  
Weighted Median
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