Image Processing

From the basics to deep learning applications

About This Series

Image processing is the technical field of manipulating and analyzing digital images mathematically. This series starts from the basic concepts of images and proceeds step by step through filtering, edge detection, morphological processing, and on to advanced image recognition using deep learning.

Image processing has become an indispensable technology across many industries, including medical diagnosis, autonomous driving, surveillance systems, and quality inspection in manufacturing.

Learn by Level

Introductory

The basics of image processing

  • What a digital image is
  • Pixels, resolution, color spaces
  • Histograms
  • Basic image operations
  • Introduction to Python/OpenCV

Basic

Filtering and transforms

  • Spatial filtering
  • Smoothing and sharpening
  • Edge detection
  • The Fourier transform and FFT
  • Noise removal

Intermediate

Image analysis and feature extraction

  • Morphological processing
  • Segmentation
  • Feature detection (SIFT, ORB)
  • Classical object detection
  • Camera models and geometry

Advanced

Deep learning and applications

  • Image classification with CNNs
  • Object detection (YOLO, SSD)
  • Semantic segmentation
  • Image generation (GANs, diffusion models)
  • Medical image processing

Learning Path

Intro Image basics Basic Filtering Interm. Feature extraction Advanced Deep learning Intro: pixels, color spaces, histograms, OpenCV basics Basic: convolution, edge detection, FFT, noise removal Interm.: morphology, segmentation, SIFT, HOG Adv.: CNNs, YOLO, segmentation, GANs
Figure 1. Learning path: progress step by step from Introductory through Basic and Intermediate to Advanced.

How the Concepts Relate

Digital image Preprocessing Filtering Freq. transform Features Segmentation Deep learning Recognition
Figure 2. Concept map of image processing: from a digital image through preprocessing, filtering, and transforms to feature extraction, segmentation, and recognition.

Main Topics

Image Basics

Pixels, resolution, color spaces (RGB, HSV, grayscale), and image formats.

Filtering

Convolution, smoothing, edge detection, and various noise-removal methods.

Feature Extraction

Feature descriptors such as corner detection, SIFT, ORB, and HOG.

Deep Learning

Image classification, object detection, and semantic segmentation with CNNs.

Related Content

Computer Vision

The field of "images → 3D and semantic understanding": feature detection (SIFT, ORB), camera models, 3D reconstruction, SLAM, and more.

CT Image Processing

Content specialized in the reconstruction and analysis of medical CT scan images.

Frequently Asked Questions

What is image processing?

Image processing is the technology of using a computer to apply various transformations, analyses, and enhancements to digital images. Its applications are wide-ranging, including noise removal, edge detection, feature extraction, segmentation, and CT reconstruction.

How are these image processing notes organized?

They are organized into four levels — introductory (intro), basic, intermediate, and advanced — plus a ct-imaging section specialized in CT scans. You can study step by step from the mathematical foundations through to implementation and applications.

What mathematical background is needed to study image processing?

You need the basics of linear algebra (matrix and vector operations, determinants), calculus (partial derivatives, integral transforms), Fourier analysis, and probability and statistics. In CT imaging, knowledge of the Radon transform and of inverse problems and optimization (such as regularization) is also useful.

What is the difference between image processing and computer vision?

Image processing mainly takes an image as input and produces a better image or a more tractable representation through filtering, transforms, restoration, and segmentation. Computer vision aims to extract an understanding of the image's content, such as feature points, 3D structure, and the meaning of objects. The two form a continuum with substantial overlap, and topics like feature detection and camera models sit at the bridge between them.