Results 91 to 100 of about 6,617 (256)
Anisotropic decompositions using representation systems based on parabolic scaling such as curvelets or shearlets have recently attracted significantly increased attention due to the fact that they were shown to provide optimally sparse approximations of
Grohs, Philipp, Kutyniok, Gitta
core +1 more source
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
Analysis of Inpainting via Clustered Sparsity and Microlocal Analysis [PDF]
Recently, compressed sensing techniques in combination with both wavelet and directional representation systems have been very effectively applied to the problem of image inpainting.
King, Emily J. +2 more
core
Deep learning‐based methods for detecting defects in cast iron parts and surfaces
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
A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform [PDF]
Shanshan Shi +3 more
openalex +1 more source
mBCCf: Multilevel Breast Cancer Classification Framework Using Radiomic Features
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
Shearlets on Bounded Domains [PDF]
Shearlet systems have so far been only considered as a means to analyze $L^2$-functions defined on $\R^2$, which exhibit curvilinear singularities. However, in applications such as image processing or numerical solvers of partial differential equations the function to be analyzed or efficiently encoded is typically defined on a non-rectangular shaped ...
Kutyniok, Gitta, Lim, Wang-Q
openaire +2 more sources
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
Sparse Regularization Based on Orthogonal Tensor Dictionary Learning for Inverse Problems
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
Different Faces of the Shearlet Group [PDF]
Recently, shearlet groups have received much attention in connection with shearlet transforms applied for orientation sensitive image analysis and restoration. The square integrable representations of the shearlet groups provide not only the basis for the shearlet transforms but also for a very natural definition of scales of smoothness spaces, called ...
Dahlke, Stephan +5 more
openaire +4 more sources

