应用离散和快速傅里叶变换提高多尺度图像分析速度

TEM Journal Pub Date : 2024-02-27 DOI:10.18421/tem131-36
V. Ďuriš, V. Semenov, S. Chumarov
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引用次数: 0

摘要

论文比较了使用不同小波进行重建的精度,作者还同时使用快速和离散傅里叶变换来计算频域中的正向和反向连续小波变换。由于使用了频域计算,因此可以高性能、高精度地进行分解、重建、图像滤波和其他变换。为了进行多尺度信号分析,我们构建了一种具有矩形幅频响应的小波,与 Matlab 计算机数学中的 Mallat 算法相比,它可以提高分解和重建的精度。同时,与 Mallat 算法相比,多尺度分析的时间缩短了数倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Discrete and Fast Fourier Transforms to Increase the Speed of Multiscale Image Analysis
The paper compares the accuracy of reconstruction using different wavelets, and the authors also use fast and discrete Fourier transforms together to calculate the forward and inverse continuous wavelet transform in the frequency domain. Due to the use of calculations in the frequency domain, it becomes possible to perform decomposition, reconstruction, image filtering, and other transformations with high performance and precision. For multiscale signal analysis, a wavelet with a rectangular amplitude-frequency response has been constructed, which allows for an increase in the accuracy of decomposition and reconstruction compared to the Mallat algorithm presented in Matlab computer mathematics. At the same time, the time of multiscale analysis is reduced several times compared to the Mallat algorithm.
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