[2410.20304] Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application
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arXiv:2410.20304 (cs)
[Submitted on 27 Oct 2024]

Title:Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application

Authors:Weiche Hsieh, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Silin Chen, Ming Liu
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Abstract:Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains.
Comments: 293 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
Cite as: arXiv:2410.20304 [cs.CV]
  (or arXiv:2410.20304v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2410.20304

Submission history

From: Ming Liu [view email]
[v1] Sun, 27 Oct 2024 02:00:33 UTC (178 KB)
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