Many conventional image processing algorithms such as noise filtering, sharpening and deblurring, assume a noise model of Additive White Gaussian Noise (AWGN) with constant standard deviation throughout the image. However, this noise model does not hold for images captured from typical imaging devices such as digital cameras, scanners and camera-phones. The raw data from the image sensor goes through several image processing steps such as demosaicing, color correction, gamma correction and JPEG compression, and thus, the noise characteristics in the final JPEG image deviates significantly from the widely-used AWGN noise model. Thus, when the image processing algorithms are applied to the digital photographs, they may not provide optimal image quality after the image processing due to the inaccurate noise model. In this paper, we propose a noise model that better fits the images captured from typical imaging devices and describe a simple method to extract necessary parameters directly from the images without any prior knowledge of imaging pipeline algorithms implemented in the imaging devices. We show experimental results of the noise parameters extracted from the raw and processed digital images.
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