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

arXiv: https://arxiv.org/abs/2410.20304 · HTML: https://mlsanity.com/paper/2410.20304/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Deep Learning Model Security: Threats and Defenses](https://mlsanity.com/paper/2412.08969/) (2024)
- [Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review](https://mlsanity.com/paper/2509.17707/) (2025)

## Abstract (arXiv)

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.