🖼️

Introduction to Digital Image Processing

Mar 20, 2025

Digital Image Processing - Lecture 1

Course Introduction

  • Offered as a special topics course for undergraduate and graduate students in engineering and computer science.
  • Material primarily sourced from "Digital Image Processing," 4th edition by Khandala and Wood.
  • Topics covered in this lecture:
    • Introduction to Digital Image Processing (DIP)
    • Origins and applications of DIP
    • Fundamental steps in DIP systems
    • Elements of visual perception
    • Image sensing and acquisition
    • Sampling and quantization

What is Digital Image Processing?

  • Image Definition: A two-dimensional function, f(x, y), where x and y are spatial coordinates and f is the intensity or gray level.
  • Digital Image: When x, y, and f are finite, discrete quantities.
  • Digital Image Processing: Processing images with digital computers.
  • Elements of a digital image are pixels.
  • Levels of Processing:
    • Low-Level: Input and output are images (e.g., noise reduction, contrast enhancement).
    • Mid-Level: Input is images, output is attributes (e.g., segmentation, classification).
    • High-Level: Images processed for semantic understanding (e.g., object recognition).

Origins of Digital Image Processing

  • First applications in the newspaper industry for transmitting images.
  • Evolution of digital technologies from the 1940s (transistors) to modern ultra-large-scale integration.

Applications of Digital Image Processing

  • Electromagnetic Signal: Gamma rays, X-rays, ultraviolet, visible light, infrared, microwaves, radio waves.
  • Applications in medical imaging, astronomy, satellite imaging, and more.

Fundamental Steps in Digital Image Processing

  • Image Acquisition: Capturing images using various devices.
  • Image Filtering and Enhancement: Noise reduction, contrast enhancement.
  • Color Image Processing: Exploiting the human eye's sensitivity to color.
  • Transformations: Using methods like Fourier Transform and Wavelet Transform for processing.
  • Compression and Watermarking: Reducing size and protecting intellectual property.
  • Morphological Processing: Structure recognition in images.
  • Segmentation and Classification: Dividing images into meaningful parts.

Components of a DIP System

  • Problem domain, image sensors, processing hardware, computer systems, hard copies, image displays, mass storage, software, and networks.

Elements of Visual Perception

  • Human eye structure and function.
  • Cone and rod cells in the retina for color and low-light vision.
  • Visual sensitivity to intensity changes and simultaneous contrast.

Light and Electromagnetic Spectrum

  • Visible spectrum ranges from 400 to 700 nanometers.
  • Radiance, luminance, and brightness defined.

Image Sensing and Acquisition

  • Images created by illumination and object's energy reflection/absorption.
  • Types of sensors: Single, line, and array sensors.
  • Imaging systems in digital cameras and other devices.

Sampling and Quantization

  • Sampling: Digitizing the spatial domain by capturing discrete data points.
  • Quantization: Digitizing the intensity domain by mapping intensity values to discrete levels.
  • Resolution:
    • Spatial Resolution: Determines smallest perceptible detail.
    • Intensity Resolution: Smallest discernible change in intensity.

Effects of Resolution on Image Quality

  • Spatial Resolution: Affects detail perception, measures in pixels per unit distance.
  • Intensity Resolution: Measured in bits, affects perceived smoothness of intensity gradients.
  • Experiments show images with more shape detail need fewer intensity levels, and vice versa.

Conclusion

  • Overview of topics for further lectures, including intensity transformations and basic mathematics in DIP.

This concludes the first lecture on Digital Image Processing, providing a foundational understanding of key concepts and systems involved in the field.

Full transcript