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I am presently working with noise removal in images. If the only available information in hand is the noise affected gray scale image, how can I identify what type of noise (say, Gaussian, Poisson, speckle, uniform etc.) is present in the image? What may be the parameters that I can consider to identify the type of noise? I am using gray scale images.
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If you have spikes at 0 and 255 values (or some constant value per channel) in the histogram of the image, you likely have salt and pepper noise. You can apply a median filter to get rid of the noise, and the size of your kernel that minimizes the spikes in the histogram can inform you about the noise level.
There are three types of impulse noises. Salt Noise, Pepper Noise, Salt and Pepper Noise. Salt Noise: Salt noise is added to an image by addition of random bright (with 255 pixel value) all over the image. Pepper Noise: Salt noise is added to an image by addition of random dark (with 0 pixel value) all over the image.
Noise in an image is the presence of artifacts that do not originate from the original scene content. Generally speaking, noise is a statistical variation of a measurement created by a random process. In imaging, noise emerges as an artifact in the image that appears as a grainy structure covering the image.
One possible way is to analyze histograms of manually selected image fragments that are known to be homogeneous. Which fragments to consider depends of course on the nature of your images.
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