Abstract
Abstract:
A novel method for estimating the variance and
standard deviation of the additive white Gaussian noise contained
in an image will be presented. Only a single image is used to
estimate the noise properties. Local image outliers are discarded,
this allows us to separate the additive zero mean white Gaussian
noise contained in a noisy image from the original image
structure. Local variance estimates can then be calculated from
the extracted noise. These local variance estimates are weak and
can be influenced by misclassified image information. Robust
statistics are then used to fuse the weak local variance estimates
to obtain a robust global noise variance estimate. This method of
estimating the noise properties is computationally efficient and
provides reliable estimation results in synthetic and real-world
imagery. The accuracy and processing complexity of the proposed
algorithm will be compared against the current state-of-the-art
noise estimators.