CS4802
Links

Week 1 Human Vision and Imaging

Course Module for the Federal Aviation Administration

Wikipedia

Misc. Color Spaces

Notes from courses offered elsewhere

Week 2 Point Processing

  • http://www.cis.rit.edu/class/simg782/lectures/lecture_05/lec782_05_05.pdf This is a lecture from the Rochester Institute of Technology. It contains the following:
    • A specific definition of Point Processing.
    • Common Point Transforms (e.g. gamma, negative image, histogram)
    • Examples on how to preform a Histogram Equalization and how to determine a what a Uniform Histogram Image looks like.
    • Describes an equalization curve being applied to an image, with some sample code. I do not know what language the code is for.
    • Describes the data that is found on various color planes (e.g. Red, Green Blue) and a comparison of what each equalization looks like.
  • http://ia600307.us.archive.org/7/items/Lectures_on_Image_Processing/EECE253_03_PointProcessing.pdf
    • This is a page that describes examples of point processes (examples of changing gamma, brightness, contrast or using a histogram).
    • There is an example of a histogram split apart, with detail showing what each of the layers look like, and the amount of data on each layer. It also gives information on what the histogram of a color image would look like. The material in here is quite thorough and well displayed, albeit a bit hard to understand with some of the mathematical notation. This is part of a series of lectures which can all be found on the left sidebar here
  • http://www.scribd.com/doc/36208391/Point-Processing
    • This gives examples of Contrast Stretching, Image Enhancement, Histogram Matching and Equalization.
    • This also gives a brief example of an image as a function
  • http://en.wikipedia.org/wiki/Point_process
    • This article gives much deeper examples of point processing including mathematical proofs and examples for many point processes.
    • Examples are given of areas where point processes might be used outside of Image Processing
  • http://bme.med.upatras.gr/improc/enhancement_point_processing.htm
    • This site has a series of examples of what Point Processing looks like in a medical environment.
    • More examples are given, as well a series of in depth articles regarding the each of the particular topics. Included amongst these are Histograms, Equalization, Image Subtraction and Averaging.

Week 3 Digital Filtering

Notes from other courses

Filtering Algorithms

Wikipedia

Code Samples

Week 5 Image Scaling

  • Wikipedia - Image Scaling
    overview with links to specific algorithms, such as:
  • Quick image scaling algorithms
    Discussion on Bresenham algorithm as well as code snippets of scaling with multiple methods
  • Depixelizing Pixel Art [PDF]
    A Microsoft research paper on keeping smooth pictures during scaling
    Extremely thorough, but very interesting
  • Image Resizing - outperform GDI+
    A very good article with sample project on many different filters
    • Box equivalent to Nearest Neighbor on upsampling, averages pixels on downsampling
    • Triangle equivalent to Low; the function can be called Tent function for its shape
    • Hermite use of the cubic spline from Hermite interpolation
    • Bell attempt to compromise between reducing block artifacts and blurring image
    • CubicBSpline most blurry filter (cubic Bezier spline) - known samples are just "magnets" for this curve
    • Lanczos3 windowed Sinc function (sin(x)/x) - promising quality, but ringing artifacts can appear
    • Mitchell another compromise, but excellent for upsampling
    • Cosine an attemp to replace curve of high order polynomial by cosine function (which is even)
    • CatmullRom Catmull-Rom curve, used in first image warping algorithm (did you see Terminator II ?)
    • Quadratic performance optimized filter - results are like with B-Splines, but using quadratic function only
    • QuadraticBSpline quadratic Bezier spline modification
    • CubicConvolution filter used in one example, its weight distribution enhances image edges
    • Lanczos8 also Sinc function, but with larger window, this function includes largest neighborhood
      •
  • The myth of infinite detail: Bilinear vs. Bicubic
    A good comparison between bilinear and bicubic resizing, and from the same guy but about fractal resizing: Better Image Resizing

Week 6 Halftoning

Week 7 Frequency Domain

Week 8 Image Compression

Lossless

Huffman Coding

Run-Length Encoding

Modified Huffman Coding

Lempel-Ziv Coding

Arithmetic Coding

Lossy

Dynamic Window-Based Run Length Encoding

Block Truncation Coding

Transform Coding

Last modified: Friday, 02-Jan-2015 10:33:32 EST