CS 117 Assignment: Median Filters

Due 11:59PM Monday, February 28

Photographs are often a mess. Sometimes there's a a hair or a scratch on the negative when you print a photograph. Still photographs taken from television broadcasts or from worn video tapes are often filled with static. Even photos taken with digital cameras might show signs of scratches or smudges on the lens. There are many ways an image can accumulate pixels that show "noise" instead of the intended subject.

One way to eliminate some kinds of noise is a median filter. The idea behind a median filter is that most pixels are surrounded by pixels of the same or similar color. If one pixel has an unexpected color (white for a scratch on the lens or black for a hair on the negative, for example), the surrounding pixels are probably still okay. If we somehow let a pixel's neighbors vote on the pixel's proper color, we might be able to get rid of noisy pixels.

Here's one way to arrange such a vote. Start with a grayscale image. Cycle through all the pixels in the image with the usual double loop over the rows and columns of the image. For each pixel P:

• Collect a list of neighbors of P. The neighborhood could be a square of a particular radius, for example, where the radius-1 neighborhood would consist of P itself plus the eight surrounding pixels.
• Sort the pixels you have collected, smallest gray level to largest.
• Pick the middle pixel of your sorted list. The gray level of this pixel is the median gray in the neighborhood. Use that median value as the new color for P.

The left image below is a gray version of our old standard photo from the London Underground, with some artificial scratching added to. Using the median filter described above, with radius-1 square neighborhoods, we get the image on the right. At the cost of a bit of blurring and flattening of the grays, we have nearly eliminated the severe damage of the diagonal scratches.

It's interesting to see that some images clean up better than others. In this scene from a spring break past, for example, the same median filter doesn't do nearly as good a job of fixing the problem (any ideas why?).

Your mission

For this assignment, write a class called ImageProcessor3. You may, if you wish, copy your ImageMultiProcessor class and just add to it. In any case, your class should include:

• public ImageProcessor3( String filename ) throws IOException
  This is the constructor. The string is the source image filename.
• public void scratch()
  This method should apply artificial scratching of some kind to the image. Diagonal white lines would be fine, but feel free to be creative.
• public void filter( int radius )
  This method should apply the median filter described above, using square neighborhoods of the specified radius. A radius-1 neighborhood will be a 3-by-3 square, a radius-2 neighborhood will be 5-by-5, etc.
• public void gray()
  You'll need this to make sure your image is gray before you begin filtering it. Go ahead and use your gray method from the last image processing assignment.

Advice

• You might find it helpful to create a simple class called Pixel to store the row, column, and gray value of a single pixel. Then you could use an ArrayList<Pixel> to store and sort the neighborhood.
• The ArrayList class has a clear method that allows you to start over with an empty list.
• The neighborhoods of pixels near the borders of the image will not be squares. Don't forget to build your neighborhood out of only those pixels that are within the boundaries of the image. Otherwise, you'll end up with those pesky ArrayIndexOutOfBoundsExceptions.
• Start early, ask questions if you have them, and have fun.