Out on Sept. 30, 2013, Due on Tue. Oct. 15, 2013. 100%+5% bonus
The images can be downloaded as a zip pack.
(1) Laplacian and Unsharp Masking (25%)
Show that subtracting the Laplacian from an image is proportional to unsharp masking. Use the definition for the Laplacian in the discrete case to derive the equivalence relation, constant weighting factors should not affect your conclusion.
(2) Spatial processing for photo enhancement (25%)
(2.1) Implement a function for histogram equalization and use it on "pout.png" below (do not use the histeq() function in MATLAB, develop code by yourself) (2.1.1) [10%] Include your source code and the equalized image. Plot the intensity transformation function u vs. v obtained from the equalization function. Compare the results with histeq() in matlab image processing toolbox. (2.1.2) [5%] Experiment with contrast stretching with matlab or in an image editing software (e.g. GIMP, or one that you prefer) comment on the outcome in comparison with (2.1.1).
(2.2) [10%] Sharpen the input image leaf.jpg below, using a combinations of techniques in (2.1), and other matlab functions such as fspecial, filter2, etc. Compare your result with those from an image processing software, or the image leaf2.jpg.
(2.3) [5% bonus] Take an image (photo you took, medical image, or images from the web), enhance it with spatial processing. Submit the "before" and "after" (as in 2.2), discuss the steps and why it looks better.
(3) High-frequency emphasis and histogram equalization (25%)
G&W 3rd Ed: Problem 4.39 page 310
(4) DFT and DCT on images (25%)
In this homework, we want to analyze the energy distributions of different types of images(for both images: banboon and monkeyking). You can use MATLAB build in functions.
(4.1) [15%] Convert the input M-by-N color image to the grayscale format. Plot the 2-D log magnitude of the 2D DFT and DCT of the grayscale image, with center shifted. Visually compare and comment on the similarity/differences among the images using the two transforms (use both images).
(4.2) [10%] Apply the truncation windows discussed in the class to keep 25% and 6.25% (1/4 and 1/16) of the DFT and DCT coefficients, i.e. two different ratios for each transform. This truncation is done by keeping the coefficients of the lowest frequencies (those within a centered smaller rectangle of (M/2)x(N/2) and (M/4)x(N/4) on the shifted FFT, respectively). Apply the 2D inverse DFT and inverse DCT to reconstruct the image for each of the truncated spectra. Plot the reconstructed images visually examine and comment on the effects of truncation (use both images).
Attachment:- 354509_2_HW2-Images.zip