Digital Image Processing Lecture 1

Course Introduction

• Course offered as a special topics course for undergrad and graduate students in engineering and computer science.
• Based on "Digital Image Processing" by Gonzalez and Woods (4th edition).

Topics Covered

• Introduction to Digital Image Processing (DIP)
• Origins of DIP
• Applications of DIP
• Fundamental steps in DIP
• Elements of visual perception
• Light and electromagnetic spectrum
• Image sensing and acquisition
• Sampling and quantization

What is Digital Image Processing?

• Image Definition: A two-dimensional function f(x,y) representing intensity or gray level at coordinates.
  • Continuous or discrete values.
• Digital Image: An image with finite, discrete values.
• DIP: Processes involving digital images via digital computers.
• Levels of DIP:
  • Low-level: Input/output are images (e.g., noise reduction).
  • Mid-level: Input images, output attributes (e.g., segmentation).
  • High-level: Semantic understanding (part of computer vision).

Origins of Digital Image Processing

• Early application in the newspaper industry for image transmission.
• Digital computers' foundation in the 1940s.
• Advancements in computers parallel DIP advancements.
• Early applications in space exploration and medical imaging (e.g., CT scans).

Applications of Digital Image Processing

• Numerous applications categorized by image sources:
  • Electromagnetic: Gamma rays, X-rays, UV, visible, infrared, microwaves, radio waves.
  • Acoustic and Ultrasonic
  • Electronic: Scanning electron microscopes.
  • Computer-generated images

Electromagnetic Spectrum

• Defined by sinusoidal waves or streams of particles (photons).
• Energy equation: E = hf, where h is Planck's constant, f is frequency.
• Regions: Gamma rays, X-rays, UV, visible, infrared, microwaves, radio waves.

Image Processing Steps

• Image Acquisition: Specialized tools depending on the spectrum used.
• Image Filtering and Enhancement: Noise reduction, contrast improvement.
• Image Restoration: Advanced noise reduction, blur artifact reduction.
• Color Image Processing: Pseudo-color to enhance perception.
• Transforms: Fourier, wavelets, etc., for advanced processing.

Components of DIP System

• Problem domain, image sensors, specialized hardware, computer processing, displays, storage, software, networking/cloud.

Visual Perception

• Human Eye Structure: Cornea, iris, lens, retina, etc.
• Cell Types:
  • Cones: Color-sensitive, concentrated around fovea.
  • Rods: Low-light sensitive, no color, spread across retina.
• Adaptation: Eye's sensitivity changes based on brightness.

Light and Electromagnetic Spectrum

• Visible spectrum: 400-700 nm.
• Colors perceived by light reflection/absorption.

Image Sensing and Acquisition

• Defined by illumination source and scene elements.
• Sensing Element: Converts energy to electric signals.
• Types: Single sensor, line sensor, array sensor.

Sampling and Quantization

• Sampling: Digitizes spatial domain.
• Quantization: Digitizes function domain.
• Resolution:
  • Spatial: Pixels per unit distance.
  • Intensity: Smallest change in intensity level.

Image Representation

• Images can be represented as 3D or 2D functions, or matrices.
• Spatial Resolution Sensitivity: More sensitive to shape variations.
• Intensity Resolution Sensitivity: More sensitive to lighting variations.

Conclusion

• Sampling and intensity levels affect perceived image quality.
• Next topics: Intensity transformation and mathematics in DIP.