CAP5415 - Digital Image Processing: Filtering and Noise Reduction - Lecture 3

Explore the fundamentals of image processing in this comprehensive lecture on filtering techniques. Delve into the digitization process of 1D functions and arcs, understanding the intricacies of gray scale and color digital images. Learn about image histograms, intensity profiles, and various types of image noise, including Gaussian, uniform distribution, and salt and pepper noise. Examine image filtering methods, focusing on derivatives and averages, discrete derivatives, and finite differences in both 1D and 2D contexts. Investigate the concepts of correlation and convolution, with a special emphasis on Gaussian filters. This in-depth lecture provides a solid foundation for understanding and applying essential image processing techniques in computer vision and digital image analysis.

Intro
Outline
Digitization of 1D function
Digitization of an arc
Gray scale digital image
Definition
RGB Channels
Sampling
Quantization
Resolution
Gray scale image
Color image
Image - other examples
Image Histogram
Histogram Example
Intensity profiles for selected (two) rows
Image noise
Gaussian Noise
Uniform distribution
Salt and pepper noise
Image filtering
Derivatives and Average
Discrete Derivative / Finite Difference
Derivative in 2-D
Derivative of Images
Averages
Example: Finite Difference
Correlation (linear relationship)
Correlation and Convolution
Gaussian filter