Results 51 to 60 of about 272,612 (359)
Multicomponent MR Image Denoising [PDF]
International Journal of Biomedical Imaging, 2009 Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality.
Manjn, José V. +5 more
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Deep Orthogonal Transform Feature for Image Denoising
IEEE Access, 2020 Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce
Yoon-Ho Shin +3 more
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Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising
, 2022 11 pages, 18 ...
Ruizhou Liu +5 more
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A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
Applied Sciences, 2020 In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it
Seung-Jun Lee, Hyuk-Yoon Kwon
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Fuzzy rule based multiwavelet ECG signal denoising [PDF]
, 2008 Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are ...
Chan, Yick-Po +5 more
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SVDD-Based Pattern Denoising [PDF]
Neural Computation, 2007 The support vector data description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects.
Park, Jooyoung +4 more
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Machine learning denoising of high-resolution X-ray nanotomography data
Journal of Synchrotron Radiation, 2022 High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic ...
Silja Flenner +6 more
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Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain [PDF]
, 2013 In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-
Aelterman, Jan +4 more
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Denoising of Image Gradients and Total Generalized Variation Denoising [PDF]
Journal of Mathematical Imaging and Vision, 2018 We revisit total variation denoising and study an augmented model where we assume that an estimate of the image gradient is available. We show that this increases the image reconstruction quality and derive that the resulting model resembles the total generalized variation denoising method, thus providing a new motivation for this model.
Komander, Birgit +2 more
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A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images
IEEE Access, 2020 Genome-wide screening using microarrays of DNA will be of great use in the early diagnosis of diseases such as cancer and HIV. It also makes use of gene discovery, pharmacogenomics, toxicogenomics, and nutrigenomics for other applications.
Steffy Maria Joseph, P. S. Sathidevi
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