Digital Image Processing — Tasrela Academy

Course Overview

This course builds a strong foundation in digital image processing, covering filtering, enhancement, segmentation, image transforms, and feature extraction. You will work hands-on with Python, OpenCV, NumPy, and image datasets to design algorithms that help computers interpret the visual world.

Learning Objectives

Understand sampling, quantization, and image representation
Apply spatial and frequency domain filtering
Perform edge detection, segmentation, and morphological processing
Extract features for recognition and measurement tasks
Build real-world image processing applications with Python/OpenCV
Prepare for advanced machine vision and deep learning courses

Module Breakdown

Module 1 — Fundamentals

Digital images, pixel operations, sampling, quantization.

Module 2 — Image Enhancement

Noise reduction, sharpening, histogram operations, contrast enhancement.

Module 3 — Frequency Domain Processing

DFT, FFT, filtering in frequency domain, convolution-correlation.

Module 4 — Edge Detection & Segmentation

Sobel, Canny, thresholding, region-based segmentation.

Module 5 — Morphological Processing

Erosion, dilation, opening, closing, boundary extraction.

Module 6 — Feature Extraction & Projects

HOG, texture, corner detection (Harris/SIFT-intro), measurement tasks.

Capstone Examples

Fabric Defect Detection
Filters + morphological ops for flaw identification.

Medical Scan Enhancement
Noise removal & contrast boosting for diagnosis support.

Document Image Cleanup
Noise removal & thresholding for OCR performance.

Tools & Technologies

Who Should Enroll

Students, engineers, and innovators curious about computer vision, robotics, AI perception, imaging systems, and research or technical entrepreneurship.

Assessment & Certification

Assignments & Labs (30%)
Module Quizzes (20%)
Capstone Project (40%)
Demo + Technical Report (10%)