Results 41 to 50 of about 4,760 (175)

Pyramidal directional filter banks and curvelets [PDF]

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002
A flexible multiscale and directional representation for images is proposed. The scheme combines directional filter banks with the Laplacian pyramid to provide a sparse representation for two-dimensional piecewise smooth signals resembling images. The underlying expansion is a frame and can be designed to be a tight frame.
Minh N. Do, Martin Vetterli
openaire   +1 more source

Performance Rate Analysis in Photovoltaic Solar Plants by Machine Learning

Advanced Energy and Sustainability Research, Volume 6, Issue 11, November 2025.
Thermal imaging and deep learning are combined to detect faults in photovoltaic panels inspected by autonomous vehicles. A robust pipeline classifies panel defects from aerial thermograms using a convolutional neural network, supporting both real‐time and offline analysis.
Alba Muñoz del Rio, Isaac Segovia Ramírez, Fausto Pedro García Márquez   +2 more
wiley   +1 more source

Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation

Medical Image Anal., 2013
Denoising of Positron Emission Tomography (PET) images is a challenging task due to the inherent low signal-to-noise ratio (SNR) of the acquired data.
A. L. Pogam, H. Hanzouli, M. Hatt, C. Rest, D. Visvikis   +4 more
semanticscholar   +1 more source

Coronal Mass Ejection Detection using Wavelets, Curvelets and Ridgelets: Applications for Space Weather Monitoring [PDF]

, 2010
Coronal mass ejections (CMEs) are large-scale eruptions of plasma and magnetic field that can produce adverse space weather at Earth and other locations in the Heliosphere. Due to the intrinsic multiscale nature of features in coronagraph images, wavelet
P. Gallagher, C. Young, J. Byrne, R. McAteer   +3 more
semanticscholar   +1 more source

Implicit Neural Representations for Unsupervised Seismic Data Interpolation From Single Gather

Geophysical Prospecting, Volume 73, Issue 9, November 2025.
ABSTRACT Missing seismic traces from data acquisition limits often significantly degrade data quality. This study presents an unsupervised method using implicit neural representation (INR), specifically sinusoidal representation network (SIREN), to enhance seismic data quality from a single shot gather.
Ganghoon Lee, Snons Cheong, Yunseok Choi
wiley   +1 more source

A Large‐Scale Dataset and Robust Multifeature Representation With Maximum Correlation‐Based Feature Fusion and Matching for Apparel Image Retrieval

Expert Systems, Volume 42, Issue 9, September 2025.
ABSTRACT Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content‐Based Image Retrieval (CBIR) research.
Marryam Murtaza, Muhammad Fayyaz, Mussarat Yasmin, Muhammad Anwar, Kashif Naseer Qureshi, Usman Ahmed Raza   +5 more
wiley   +1 more source

Seismic Amplitude Recovery with Curvelets

, 2007
A non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness and continuity constraints is proposed. The applied formalism explores curvelets as a directional frame that, by their sparsity on the image, and their invariance under the imaging operators, allows for a stable recovery of the amplitudes.
Moghaddam, P.P., Herrmann, F.J., Stolk, C.C.   +2 more
openaire   +2 more sources

Classifying Power Quality Issues in Railway Electrification Systems Using a Nonsubsampled Contourlet Transform Approach

Engineering Reports, Volume 7, Issue 8, August 2025.
Railway electrification systems indeed have unique challenges due to variable power demand and dynamic train operations. Power quality (PQ) monitoring for high‐speed trains (HSTs) is essential to guarantee the effectual and unfailing operation of the ESs.
Pampa Sinha, Kaushik Paul, I. M. Elzein, Mohamed Metwally Mahmoud, Ali M. El‐Rifaie, Wulfran Fendzi Mbasso, Ahmed M. Ewais   +6 more
wiley   +1 more source

Hybrid Radiomics and Machine Learning for Brain Tumors Multi‐Task Classification: An Exploratory Study on Integrating GLCM and Curvelet‐Based Features for Enhanced Accuracy

Health Science Reports, Volume 8, Issue 8, August 2025.
ABSTRACT Background and Aims Accurate classification of brain tumors is vital for effective treatment planning. Manual assessment of magnetic resonance imaging (MRI) scans is often subjective and time‐consuming. This exploratory study proposes a machine learning approach integrating radiomic features from contrast‐enhanced T1‐weighted MRI scans to ...
Mostafa Jafari, Masoumeh Rahmani, Sanaz Alibabaei, Jafar Fatahiasl, Sasan Razmjoo, Mohammad Javad Tahmasebi Birgani, Marziyeh Tahmasbi   +6 more
wiley   +1 more source

Data-Proximal Complementary ℓ¹-TV Reconstruction for Limited Data Computed Tomography

Mathematics
In a number of tomographic applications, data cannot be fully acquired, resulting in severely underdetermined image reconstruction. Conventional methods in such cases lead to reconstructions with significant artifacts.
Simon Göppel, Jürgen Frikel, Markus Haltmeier   +2 more
doaj   +1 more source