This project explores the mathematical and computational foundations of Signal and Image Processing through a series of MATLAB experiments. It bridges theoretical concepts like Fourier analysis, convolution, and clustering with practical implementations for image filtering, signal transformation, and compression. Each module demonstrates how abstract mathematical principles translate into powerful real-world applications across domains such as Computer Vision, Audio Analytics, and Pattern Recognition.

Key highlights include:

• Fourier Transform & Convolution – Frequency domain filtering, signal reconstruction, and spectral analysis.

• Image Processing & Compression – 2D DFT-based filtering and energy compaction for lossy compression.

• Mathematical Insight – Explains derivative and convolution theorems, emphasizing efficiency through frequency-domain computation.

Together, these implementations form a concise yet comprehensive learning toolkit for understanding how core mathematical operations drive modern signal and image processing systems.