机器学习赋能信号与图像处理，提升增强、滤波与识别效果。
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application
- 结合傅里叶变换等框架实现特征提取与数据处理
- 基于Python实现算法，支持实时高效计算
- 适合计算机视觉与AI应用开发者参考
数字信号处理（DSP）与数字图像处理（DIP）结合机器学习（ML）和深度学习（DL）已成为计算机视觉及相关领域的研究热点。本文聚焦图像增强、滤波技术和模式识别等变革性应用。通过集成离散傅里叶变换（DFT）、Z变换及傅里叶变换方法，实现鲁棒的数据操作与特征提取，支撑AI驱动任务。利用Python实现算法，优化实时数据处理性能，构建可扩展、高性能的计算机视觉解决方案。本工作展示了ML与DL在推进DSP与DIP方法学方面的潜力，助力人工智能、自动特征提取及多领域应用发展。
原文摘要 · Abstract (English)
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.
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