COS 426 - Computer Graphics

Spring 2012

Assignment 1: Image Processing

In this assignment you will create a simple image processing program. The operations that you implement will mostly be filters that take an input image, process the image, and produce an output image.

Getting Started

How the Program Works

The user interface for this assignment was kept as simple as possible, so you can concentrate on the image processing issues. The program runs on the command line. It reads an image from a file (the first program argument) and writes an image to a file (the second program argument). In between, it processes the image using the filters specified by subsequent command line arguments. For example, to increase the brightness of the image in.jpg by 10%, and save the result in the image out.jpg, you would type:

% imgpro in.jpg out.jpg -brightness 1.1

For each available image filter there may be one or more optional parameters (e.g., brightness takes a factor). To see the complete list of filters and their parameters, type:

% imgpro -help

If you specify more than one filter on the command line, they are applied in the order that they are found. For example,

% imgpro in.jpg out.jpg -contrast 0.8 -scale 0.5 0.5

What You Have to Do

• Per-pixel Operations
  • (1/2) Black & White: Convert to gray levels by replacing each pixel with its luminance.
  • (1/2) Gamma: Apply a gamma correction to the image.
  • (1/3) Saturation: Change the saturation of an image. See Graphica Obscura.
  • (1/3) Contrast: Change the contrast of an image. See Graphica Obscura.
  • (1/3) Extract Channel: Leave specified channel intact and set all others to zero.
• Linear Filtering Operations
  Note: Since all the filters below are intended to operate on linear pixel values, be sure to undo the image gamma (by raising values to the power of 2.2) before processing, and re-apply gamma (by raising values to the power of 1/2.2) afterwards. Use good coding practices (modularity) and don't re-implement gamma correction, given that you already implemented it, as specified above.
  • (1) Gaussian Blur: Blur an image by convolving it with a Gaussian low-pass filter.
  • (1/3) Sharpen: Apply a linear sharpening filter to the image.
  • (1/3) Edge detect: Detect edges in an image by convolving it with an edge detection kernel.
  • (1/3) Motion Blur: Apply a left-right motion blur to the image.
• Non-Linear Filtering Operations
  • (1) Median Filter: Remove speckle noise using a median filter of given width
  • (2) Bilateral Filter: Smooth while preserving sharp edges in the original. See here.
• Resampling Operations
  Note: You must provide code for point, linear, and Gaussian sampling
  Clarification: You must implement three sampling methods (point, bilinear, and Gaussian), which can be controlled by the -sampling option prior to -scale, -rotate, or -fun. For Gaussian sampling, choose sensible Gaussian filter dimensionss. For scaling, this requires adapting the extent of the Gaussian, for rotation and fun, you may use a fixed size of your choosing.
  • (2) Scale: Scale an image up or down by a real valued factor.
  • (2) Rotate: Rotate an image by a given angle.
  • (2) Fun: Warp an image using a non-linear mapping of your choice (examples are fisheye, sine, bulge, swirl).
• Dithering Operations
  • (1/3) Quantize: Change the number of bits per channel of an image, using simple rounding.
  • (2/3) Random dither: Convert an image to a given number of bits per channel, using a random threshold.
  • (2) Ordered dither: Convert an image to a given number of bits per channel, using a 4x4 ordered dithering matrix.
  • (2) Floyd-Steinberg dither: Convert an image to a given number of bits per channel, using dithering with error diffusion.
• Miscellaneous
  • (1) Composite: Compose one image with a second image using the over operator, with a third image as a matte (alpha).
  • (3) Morph: Use pairs of corresponding lines to morph a source image into a target image using a morph parameter t. See [Beier92]. Use the provided line correspondence editor to define the line correspondences.
  • (up to 3) Nonphotorealism: Implement any non-trivial painterly filter. For inspiration, take a look at the effects available in programs like xv, PhotoShop, and Image Composer, or GIMP (e.g., impressionist, charcoal, stained glass, etc.). The points awarded for this feature will depend on the creativity and difficulty of the filter. At most one such filter will receive points.
  • (up to 3) Anything else: Implement any non-trivial image processing algorithm of your own choosing, and we will give you points according to the difficulty.

By implementing all the required features, you get 14 points. There are many ways to get more points:

• implementing the optional features listed above;
• (1) submitting one or more images or movies for the art contest (movies should be in .mpeg, .mov or .avi format, animating the results of one or more filters with continuously varying parameters, e.g. the morph);
• (2) winning the art contest.

It is possible to get more than 20 points. However, implementing more than 6 points worth of non-required features incurs diminishing returns: each successive point is worth 3/4 as much as the previous one. Thus, the adjusted extra credit score is computed according to the following formula:

Extra credit points cannot replace the required features (bold items). Your final score will be calculated by adding your score on the required features (up to 14 points) to your score on the optional features (up to 9 points, with diminishing returns past 6).

Submitting

This assignment is due Thursday, February 23, 2012 at 11:59 PM. Please see the general notes on submitting your assignments, or the same notes explained in slides, as well as the late policy and the collaboration policy.

Please submit a single .zip file named [your NetID]_cos426_assignment1.zip containing

• your writeup.html, containing links to all result images and a description of your implementation;
• your complete output/ and modified src/ directory trees. Please submit images in .jpg format, and remove binaries and object files from the source tree (i.e., leaving only source code). To do this, run make clean or execute "Clean Solution" from the build menu in Visual Studio.

The Dropbox link to submit your assignment is here.

writeup.html should be an HTML document demonstrating the effects of the features you have implemented. For some features (e.g., black & white), you can simply show the input and output of your program. However, for features that take an input parameter (e.g., blur), you should provide a series of images showing at least three settings of the input parameter to demonstrate that your code is working properly. You can start from the skeleton writeup.html provided with the distribution as a template -- simply add sections to that HTML file for the features you implement in the following format:

• the name of the feature on the top line,
• the input image and output image(s) on a second line,
• text listing the command(s) used to generate the output images on the following lines, and
• (optionally) comments about what to look for in your output (including possible problems in the implementation).

We remind you that you are expected to use good programming style at all times, including meaningful variable names, a comment or three describing what the code is doing, etc. We will test your code by running make in your src/ subdirectory, followed by make in the main assignment directory to create the output images. Please ensure that your code builds/runs in this way.