Digital Image Processing | CS | Course

Brief Information
  • Name : Digital Image Processing
  • Lecturer : Lee Yun-Gu
  • Semester : 2014 Fall
  • Course : BE. Computer Science and Engineering
  • Textbook : Gonzalez, R., C. and Woods, R., E. (2007) Digital Image Processing. 3rd E. Prentice Hall.
  • Syllabus [link]
Trace lectures

Listing themes I learned in the lecture in time order.

  • Image compression
  • data redundancy
  • coding redundancy
  1. Create image files and zoom in/out and rotation.
    1. Create image files: polygons, gradation.
    2. Zoomed in & out image files using bilinear interpolation: 256*256 to 436*436 and 512*512.
    3. Create rotated image files: cases of 30, 45, 60 degree.
  2. Histogram equalization, Smoothing, Sharpening, Median filter
    1. Histogram equalization
    2. Smoothing: average filter 3*3, average filter 7*7, weight average filter 3*3, and their comparison.
    3. Sharpening: negative weight average filter 3*3
    4. Median filtering: median filter 3*3
  3. Implement a 2D-DFT function and its inverse using C language. And implement a smoothing function in frequency, a lowpass filter.
    1. Implement 2D-DFT function and its inverse function
    2. Output smoothing images using lowpass filters of 5 different radiuses in frequency domain.
Summarize themes


My comments
  • statistics, discretelty converting, signal processing: helpful to understand and develop image processing idea.

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