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Up: Noise Estimation and Removal
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Use the create Tool to generate an image with uniform random noise on each pixel.
Threshold the resulting image to obtain a binary image with 10 percent zero values.
Multiply an image by this mask image. This will generate a simulated effect of pixel drop out.
- Now apply the median filter and again assess the quantity of remaining pixel noise.
Repeat the experiment for various percentages of drop out.
- Plot a graph of the number of remaining drop out pixels as a function of the percentage
of simulated drop out. At what point does the algorithm begin to fail to remove all drop out noise?