A curated collection of Image Processing course assignments implemented in Python. Each sheet contains self-contained exercises, reproducible code, and visual outputs to help you understand classic and modern techniques step-by-step.
Each folder corresponds to one assignment “sheet”. You’ll typically find Python scripts and/or notebooks plus input images for experiments. (Repo overview shows two sheets and Python as the primary language.) :contentReference[oaicite:0]{index=0}
- Apply fundamental image processing operations (point operations, filtering, edge/feature detection, morphology).
- Understand image transforms (Fourier/DFT, Hough, geometric transforms).
- Practice segmentation and thresholding (Otsu, adaptive, region-based).
- Compare results visually and quantitatively (PSNR/SSIM where relevant).
- Build clean, reproducible experiments.
If a sheet focuses on specific topics (e.g., histogram equalization, Gaussian vs. median filtering, canny edges), document that in the sheet’s own mini-README or notebook header.
git clone https://github.com/AhmadAlaa1/Image_Processing_Assignments.git
cd Image_Processing_Assignments