WE-FG-207B-04: Noise Suppression for Energy-Resolved CT Via Variance Weighted Non-Local Filtration

Abstract Synthetic aperture radar images play an important role in military and civilian fields, but the presence of speckle noise has an impact on subsequent tasks such as target detection and target interpretation. With the development of multi-azimuth observation mode, the obtained multi-azimuth image sequences have high similarities. Therefore, combined with multi-azimuth image sequences, a novel method of SAR image speckle noise suppression based on clustering is proposed in this paper. In this method, multi-azimuth joint filtering framework based on two-level filtering is proposed, in which pre-filtering for single image and joint filtering based on Non-local Means algorithm for multi-azimuth image are used to suppress the noise. And k-means clustering is used to optimize the search area in the multi-azimuth joint filtering, so as to effectively suppress speckle noise while retaining structural details.

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Signal preserving non-local noise suppression for photon-counting CT

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) ◽

10.1109/nssmic.2017.8532861 ◽

2017 ◽

Author(s):

Joseph Harms ◽

Lei Zhu

Keyword(s):

Noise Suppression ◽

Photon Counting ◽

Non Local

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Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging

Sensors ◽

10.3390/s19020245 ◽

2019 ◽

Vol 19 (2) ◽

pp. 245 ◽

Cited By ~ 4

Author(s):

Saba Adabi ◽

Siavash Ghavami ◽

Mostafa Fatemi ◽

Azra Alizad

Keyword(s):

Background Noise ◽

Noise Suppression ◽

Human Subjects ◽

Power Doppler ◽

Doppler Imaging ◽

Vascular Networks ◽

Non Local ◽

Spatio Temporal ◽

Diagnostic Applications

Vascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image microvessels by applying tissue clutter filtering methods on the spatio-temporal data obtained from plane-wave imaging. However, the resultant vessel images suffer from background noise that degrades image quality and restricts vessel visibilities. In this paper, we addressed microvessel visualization and the associated noise problem in the power Doppler images with the goal of achieving enhanced vessel-background separation. We proposed a combination of patch-based non-local mean filtering and top-hat morphological filtering to improve vessel outline and background noise suppression. We tested the proposed method on a flow phantom, as well as in vivo breast lesions, thyroid nodules, and pathologic liver from human subjects. The proposed non-local-based framework provided a remarkable gain of more than 15 dB, on average, in terms of contrast-to-noise and signal-to-noise ratios. In addition to improving visualization of microvessels, the proposed method provided high quality images suitable for microvessel morphology quantification that may be used for diagnostic applications.

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An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

10.4018/978-1-6684-2408-7.ch055 ◽

2022 ◽

pp. 1157-1173

Author(s):

Bibekananda Jena ◽

Punyaban Patel ◽

G.R. Sinha

Keyword(s):

Neural Network ◽

Impulse Noise ◽

Noise Suppression ◽

Back Propagation ◽

Feed Forward Neural Network ◽

Suppression Technique ◽

Mean Filter ◽

Local Mean ◽

Non Local ◽

A New Technique

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

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Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.5.210 ◽

2014 ◽

Vol 5 ◽

pp. 2016-2025 ◽

Cited By ~ 6

Author(s):

Torsten Bölke ◽

Lisa Krapf ◽

Regina Orzekowsky-Schroeder ◽

Tobias Vossmeyer ◽

Jelena Dimitrijevic ◽

...

Keyword(s):

Noise Suppression ◽

Ground Truth ◽

Adaptive Method ◽

Fluorescent Nanoparticles ◽

Noise Characteristics ◽

Mucosal Tissues ◽

Noise Structure ◽

Non Local ◽

Fine Structures ◽

Data Adaptive

Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.

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