Results 41 to 50 of about 169 (141)
A self‐supervised scheme for ground roll suppression
Geophysical Prospecting, Volume 72, Issue 7, Page 2580-2598, September 2024.Abstract In recent years, self‐supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labelled data in the training stage, an unobtainable requirement for seismic data. However, current self‐supervised methods usually suppress simple noise types, such as random and trace‐wise noise ...
Sixiu Liu +5 more
wiley +1 more source
Computerized tomography with total variation and with shearlets [PDF]
Inverse Problems, 2017 Abstract To reduce the x-ray dose in computerized tomography (CT), many constrained optimization approaches have been proposed aiming at minimizing a regularizing function that measures a lack of consistency with some prior knowledge about the object that is being imaged, subject to a (predetermined) level of ...
Edgar Garduño, Gabor T. Herman
openaire +2 more sources
In vivo T2 measurements of the fetal brain using single‐shot fast spin echo sequences
Magnetic Resonance in Medicine, Volume 92, Issue 2, Page 715-729, August 2024.Abstract Purpose We propose a quantitative framework for motion‐corrected T2 fetal brain measurements in vivo and validate the single‐shot fast spin echo (SS‐FSE) sequence to perform these measurements. Methods Stacks of two‐dimensional SS‐FSE slices are acquired with different echo times (TE) and motion‐corrected with slice‐to‐volume reconstruction ...
Suryava Bhattacharya +11 more
wiley +1 more source
IET Image Processing, Volume 18, Issue 8, Page 2114-2125, 19 June 2024.This paper introduces a novel infrared and visible image fusion network to address the limitations of auto‐encoder fusion networks. In the designed network, the encoder employs a multi‐branch cascade structure with convolution kernels of different sizes to extract multi‐scale features, and the fusion layer incorporates a non‐local attention module ...
Jing Xu, Zhenjin Liu, Ming Fang
wiley +1 more source
IET Image Processing, Volume 18, Issue 5, Page 1358-1372, 17 April 2024.An adaptive neuro‐fuzzy inference system is presented based on an optimization of genetic algorithm to classify normal and abnormal brain tumours. Abstract An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours.
Marzieh Ghahramani, Nabiollah Shiri
wiley +1 more source
Democracy of shearlet frames with applications
Journal of Approximation Theory, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Compactly Supported Shearlets [PDF]
, 2011 24 pages, 7 figures, Conference Proceeding: Approximation Theory XIII (San Antonio, TX, 2010), Springer (to appear)
Kutyniok, Gitta +2 more
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Deep learning‐based methods for detecting defects in cast iron parts and surfaces
IET Image Processing, Volume 18, Issue 1, Page 47-58, 10 January 2024.First, this article used multiple data augmentation methods to alleviate the problem of small sample size in casting datasets. Second, attention mechanism was introduced. Finally, a novel feature fusion layer structure was adopted to improve the original network model.
Pengyu Wang, Peng Jing
wiley +1 more source
mBCCf: Multilevel Breast Cancer Classification Framework Using Radiomic Features
International Journal of Intelligent Systems, Volume 2024, Issue 1, 2024.Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies.
Lipismita Panigrahi +6 more
wiley +1 more source
Sparse Regularization Based on Orthogonal Tensor Dictionary Learning for Inverse Problems
Mathematical Problems in Engineering, Volume 2024, Issue 1, 2024.In seismic data processing, data recovery including reconstruction of the missing trace and removal of noise from the recorded data are the key steps in improving the signal‐to‐noise ratio (SNR). The reconstruction of seismic data and removal of noise becomes a sparse optimization problem that can be solved by using sparse regularization.
Diriba Gemechu, Francisco Rossomando
wiley +1 more source