A modified sigma filter for noise reduction in images
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Bimodal biometric person identification system under perturbations
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Modified sigma filter using image decomposition
MUSP'10 Proceedings of the 10th WSEAS international conference on Multimedia systems & signal processing
Filtering-based noise estimation for denoising the image degraded by gaussian noise
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Journal of Visual Communication and Image Representation
Efficient noise reduction in images using directional modified sigma filter
The Journal of Supercomputing
MRF-based adaptive detection approach: a framework for restoring image degraded by Gaussian noise
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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This paper proposes a fast noise estimation algorithm using a Gaussian filter. It is based on block-based noise estimation, in which an input image is assumed to be contaminated by the additive white Gaussian noise and a filtering process is performed by an adaptive Gaussian filter. Coefficients of a Gaussian filter are selected as functions of the standard deviation of the Gaussian noise that is estimated from an input noisy image. For estimation of the amount of noise (i.e., standard deviation of the Gaussian noise), we split an image into a number of blocks and select smooth blocks that are classified by the standard deviation of intensity of a block, where the standard deviation is computed from the difference of the selected block images between the noisy input image and its filtered image. In the experiments, the performance of the proposed algorithm is compared with that of the three conventional (block-based and filtering-based) noise estimation methods. Experiments with several still images show the effectiveness of the proposed algorithm. The proposed noise estimation algorithm can be efficiently applied to noise reduction in commercial image - or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.