We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.
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http://dx.doi.org/10.1109/TIP.2003.813140 | DOI Listing |
PeerJ Comput Sci
November 2024
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Computer Science and Engineering, Indian Institute of Technology, Dhanbad, 826001, India.
Heliyon
September 2024
Research Unit of Automation and Applied Computer (UR-AIA), Electrical Engineering Department of IUT-FV, University of Dschang, P.O. Box: 134, Bandjoun, Cameroon.
Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images.
View Article and Find Full Text PDFJ Imaging
August 2024
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.
Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments.
View Article and Find Full Text PDFJ Acoust Soc Am
August 2024
Department of Electrical Engineering, Colorado School of Mines, Golden, Colorado 80401, USA.
Seismic data recorded by distributed acoustic sensing (DAS) interrogator units on deployed optical fiber are being used for a variety of subsurface imaging and monitoring investigations. To reduce the costs of active-source DAS surveying applications, seismic interferometry can be applied to estimate inter-sensor wavefields from DAS records. However, recording long-term records for ambient interferometry requires considerable data storage and sections of DAS optical fibers may be unusable because of broadside sensitivity considerations from the DAS fiber orientation and due to localized coherent energy sources with amplitudes significantly larger than the ambient signal of interest.
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