@inproceedings{scholars943,
           title = {Image SNR estimation using the autoregressive modeling},
         address = {Kuala Lumpur},
             doi = {10.1109/ICIAS.2010.5716130},
         journal = {2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010},
            year = {2010},
            note = {cited By 4; Conference of 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010 ; Conference Date: 15 June 2010 Through 17 June 2010; Conference Code:84196},
        keywords = {Additive white noise; AR models; Auto-correlation value; Auto-regressive; Autoregressive modeling; Cross-correlation function; Linear prediction; Predictor coefficients; Second order statistics; Signal to noise ratio estimation; Single images; SNR estimation; SNR values, Computer simulation; Signal to noise ratio; White noise, Estimation},
          author = {Kamel, N. and Kafa, S.},
        abstract = {A number of techniques have been proposed during the last two decades for Signal-to-Noise Ratio (SNR) estimation in images. The majority of these techniques are based on the cross-correlation function of two images of the same area. However, the need for two images to estimate SNR value confines these techniques to non-stored images and thus limits their applications. In this paper the second order statistics of image corrupted by additive white noise are modeled by Autoregressive-model and the relationship between AR model and linear prediction is utilized in estimating the predictor coefficients. The predictor is then used to estimate the zero-offset autocorrelation value and accordingly obtain the power of the noise-free image. Unlike others, the proposed technique is based on single image and offers the required accuracy and robustness in estimating the SNR values.},
            isbn = {9781424466238},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952753385&doi=10.1109\%2fICIAS.2010.5716130&partnerID=40&md5=a90515c1da48b3f0a3bc0a2eafbbf121}
}