The simulated noise used to benchmark wavelet edge detection in this work was described incorrectly. The correct description is given here, and new results based on noise that matches the original description are provided. The results support our original conclusion, which is that wavelet edge detection outperforms thresholding in the presence of white noise and 1/noise.
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http://dx.doi.org/10.1088/1361-6528/ac4284 | DOI Listing |
Sensors (Basel)
December 2024
ITA Technological Institute of Aeronautics, Electronic and Computer Engineering, São José dos Campos 12228-900, SP, Brazil.
There is extensive use of nondestructive test (NDT) inspections on aircraft, and many techniques nowadays exist to inspect failures and cracks in their structures. Moreover, NDT inspections are part of a more general structural health monitoring (SHM) system, where cutting-edge technologies are needed as powerful resources to achieve high performance. The high-performance aspects of SHM systems are response time, power consumption, and usability, which are difficult to achieve because of the system's complexity.
View Article and Find Full Text PDFJ Imaging Inform Med
December 2024
Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, India.
Curr Med Imaging
December 2024
Affiliated Tumor Hospital, Xinjiang Medical University, Ürümqi, 830011, China.
Background: Currently, most multimodal medical image fusion techniques focus solely on integrating the edge details of image features, often overlooking color preservation from the source images. Hence, this paper proposes a multi-channel fusion algorithm based on gradient domain-guided image filtering.
Purpose: This study aims to enhance the color preservation of source images in multimodal medical image fusion algorithms.
J Chem Theory Comput
December 2024
Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China.
A point cloud filtering method is presented for atmospheric layer detection from lidar data. The method involves rising edge event recognition based on a wavelet transform function. Density-based clustering was then utilized to separate the real boundary from the original noisy point clouds based on continuous distribution characteristics of cloud and aerosol layer.
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