Digital Image processing Lecture Notes

Description: This is an 18-lecture series on Image Processing.This introductory course in image processing should give the student a working knowledge of the most commonly used methods and procedures for image enhancement and restoration. The emphasis of the course is on practical results: given an image and a goal for its processing (e.g., feature enhancement, color correction, sharpening, warping, etc.) the student should be able to select and implement an appropriate procedure to achieve that goal. Good practical results often depend on an understanding of the mathematics behind the procedures as well as the ability to write software to implement the mathematics. Thus, there are significant mathematical and computational components to the course. In the past, most students have spent most of their time associated with this course writing and debugging computer programs.

Recommended but not required: An introductory course in digital signal processing (such as EECE 214 or EECE 252) and proficiency in writing computer programs in C, C++, Matlab, or Mathematica. Matlab is used in the class and the labs.

Lecture 01: Introduction to the Course (pdf)
Lecture 02: Digital Image Processing with Matlab (pdf)
Lecture 03: Point Processing of Images (pdf)
Lecture 04: Color Perception (pdf)
Lecture 05: Color Correction (pdf)
Lecture 06: The Fourier Transform (1-D & 2-D) (pdf)
Lecture 07: Spatial Filtering -- Image Convolution (pdf)
Lecture 08: Spatial Filtering in the Frequency Domain (pdf)
Lecture 09: Image Sharpening (pdf)
Lecture 10: Pixelization and Quantization (pdf)
Lecture 11: Image Sampling and Aliasing (pdf)
Lecture 12: Image Resampling (pdf)
Lecture 13: Image Rotation (pdf)
Lecture 14: Image Noise Reduction: Uncorrelated Noise (pdf)
Lecture 15: Image Noise Reduction: Correlated Noise (pdf)
Lecture 16: Mathematical Morphology -- Median FIlters (pdf)
Lecture 17: Mathematical Morphology -- Binary Images (pdf)
Lecture 18: Mathematical Morphology -- Grayscale Images (pdf)