On the digital image additive white Gaussian noise estimation

Digital imaging is widely used in applications such as medical, biometrics, multimedia,...etc. In many cases, images are transmitted through Internet from one point to another. During image acquisition and transmission, factors such as moving objects, sensor quality, and channel interferences may result in additive noise. The presence of noise affects image quality. Image denoising process attempts to reconstruct a noiseless image and improve its quality. Denoising an image with additive white Gaussian noise (AWGN) is a challenging process. Parameters such as noise mean and variance provide noise characteristics of AWGN. This paper compares three different algorithm for noise estimations; ant colony optimization, fuzzy logic, and region merging. It is shown that region merging algorithm provides better results with less resources and minimum computation time.

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