Noise suppression may be one of the most relevant realms of image enhancement. There are all kinds of noise, and even digital photographs are not immune to it. Usually the algorithms that deal with noise are grouped into two categories: those that deal with spurious noise (often called shot or impulse noise), and those that deal with noise that can envelop a whole image (in the guise of Gaussian-type noise). A good example of the latter is the “film grain” often found in old photographs. Some might think this is not “true” noise, but it does detract from the visual quality of the image, so should be considered as such.
Below is an example of an image with Gaussian noise. This type of noise can be challenging to suppress because it is “ingrained” in the structure of the image.
Here are four different attempts at trying to suppress the noise in the image:
- A Gaussian blurring filter (σ=3)
- A median filter (radius=3)
- The Perona-Malik Anisotropic Diffusion filter
- Selective mean filter
To show the results, we will look at the extracted regions from some of the algorithmic results compared to the original noisy image:
It is clear the best results are from the Perona-Malik Anisotropic Diffusion filter , which has suppressed the noise whilst preserving the outlines of the major objects in the image. The median filter has performed second best, although there is some blurring which has occurred in the processed image, which letters in the poster starting to merge together. Lastly, the Gaussian blurring has obviously suppressed the noise, whilst incorporating significant blur into the image.
Suppressing the form of noise is not a trivial task. The Perona-Malik approach performs the task incredibly well, by removing image noise, without significantly affecting parts of the image content.
 Perona, P., Malik, J., “Scale-space and edge detection using anisotropic diffusion”, In: Proceedings of IEEE Computer Society Workshop on Computer Vision,. pp.16–22. (1987)