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.
@article{arxiv.2410.20304,
title = {Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application},
author = {Weiche Hsieh and Ziqian Bi and Junyu Liu and Benji Peng and Sen Zhang and Xuanhe Pan and Jiawei Xu and Jinlang Wang and Keyu Chen and Caitlyn Heqi Yin and Pohsun Feng and Yizhu Wen and Tianyang Wang and Ming Li and Jintao Ren and Xinyuan Song and Qian Niu and Silin Chen and Ming Liu},
journal= {arXiv preprint arXiv:2410.20304},
year = {2025}
}