A bayesian multi-frame image super-resolution algorithm using the Gaussian Information Filter

Woods, Matthew; Katsaggelos, Aggelos K.

doi:10.1109/icassp.2017.7952380

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…Generalized MFNR Algorithm can remove most kinds of noise including gaussian noise in cameras, speckle noise in laser and ultrasound images, gamma noise in radar applications, or any other application. Thus, this Algorithm can be successfully applied in the fields of medicine, astronomy, radar with better performance than existing image noise removal tools [17]. For example, in cameras, the Gaussian Noise become especially a nuisance in low light condition, where even standard MFNR Algorithms fail.…”

Section: B Performance Analysismentioning

confidence: 99%

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

Nazir¹,

Younis²,

Shahzad³

2020

Preprint

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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

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Section: B Performance Analysismentioning

confidence: 99%