Experimental measurement of noise-removal techniques for Compton backscatter imaging systems as applied to the detection of landmines

1996 ◽  
Author(s):  
Joseph C. Wehlburg ◽  
Shyam P. Keshavmurthy ◽  
Edward T. Dugan ◽  
Alan M. Jacobs
Keyword(s):  
Experimental Measurement ◽  
Noise Removal ◽  
Imaging Systems

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

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.

Digital imaging and quantitative image analysis of polymer blends

1996 ◽  
Vol 54 ◽  
pp. 170-171
Author(s):  
Xiao Zhang
Keyword(s):  
Digital Imaging ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Polymer Science ◽  
Key Factors ◽  
Multiple Imaging ◽  
Quantitative Image ◽  
Digital Imaging Systems ◽  
Multi Phase ◽  
Network Technologies

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.

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