It is important to estimate the noise of digital image quantitatively and efficiently for many applications such as noise
removal, compression, feature extraction, pattern recognition, and also image quality assessment. For these applications,
it is necessary to estimate the noise accurately from a single image. Ce et al proposed a method to use a Bayesian MAP
for the estimation of noise. In this method, the noise level function (NLF) which is standard deviation of intensity of
image was estimated from the input image itself. Many NLFs were generated by using computer simulation to construct
a priori information for Bayesian MAP. This a priori information was effective for the accurate noise estimation but not
enough for practical applications since the a priori information didn't reflect the variable characteristics of the individual
camera depending on the exposure and shutter speed.
In this paper, therefore, we propose a new method to construct a priori information for specific camera in order to
improve accuracy of noise estimation. To construct a priori information of noise, the NLFs were measured and
calculated from the images captured under various conditions. We compared the accuracy of noise estimation between
proposed method and Ce's model. The results showed that our model improved the accuracy of noise estimation.