Results 71 to 80 of about 640 (174)
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
IEEE Access, 2019 Conventional objective image assessment metrics, such as mean squared error and peak signal-to-noise ratio, which only calculates pixel-based differences between the original and the degraded images, are not in agreement with the human vision.
Wu Dong, Hongxia Bie, Likun Lu, Yeli Li
doaj +1 more source
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
Frontiers in Remote SensingSatellite image analysis is essential for remote sensing analysis. Two types of data are captured via satellite: Synthetic Aperture Radar (SAR) imagery (which has structure) and multispectral imagery (which contains spectral information), so ...
Sai Bhargav Kasetty, Rajakumar Krishnan
doaj +1 more source
Construction of Meyer Wavelet Using Fully Smooth Sigmiod Function
Journal of Harbin University of Science and Technology, 2019 In order to obtain better smooth effect in signal or image reconstruction, the regularity or continuous differentiability of wavelet must be increased as much as possible.
SHAO Yun-hong, DENG Cai-xia, HE Peng
doaj +1 more source
Image Sequence Fusion and Denoising Based on 3D Shearlet Transform
Journal of Applied Mathematics, 2014 We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal ...
Liang Xu, Junping Du, Zhenhong Zhang
doaj +1 more source
Compactly supported shearlets are optimally sparse
Journal of Approximation Theory, 2011 Cartoon-like images, i.e., C^2 functions which are smooth apart from a C^2 discontinuity curve, have by now become a standard model for measuring sparse (non-linear) approximation properties of directional representation systems. It was already shown that curvelets, contourlets, as well as shearlets do exhibit (almost) optimally sparse approximation ...
Gitta Kutyniok, Wang-Q Lim
openaire +3 more sources
IEEE Access, 2019 Due to the limitation of the seismic data acquisition environment and instrument, seismic data are often subjected to random noise interference. At the same time, random noise is inevitably introduced in the processing of seismic data.
Wen-Long Hou +5 more
doaj +1 more source