Abstract
In this paper, we propose a noise level evaluation method for real captured photos. Different from conventional noise removal methods that assume noise follow a simply additive Gaussian distribution, noise distribution in our method is supposed to be a more sophisticated intensity-dependent distribution, which has a better fit with actual noise model. Follow the definition of noise level function (NLF) which represents the variation of the standard deviation of noise with respect to image intensity. After exposing the close relationship between NLF and camera response function (CRF), we fit the curve of NLF with the constraints imposed by the shape of CRF. Index Terms— image noise estimation, noise level function, camera response function
Highlights
For noise contaminated photos, random noise is mainly caused by quantum effects, thermal fluctuations and dark current leakage
We have revealed the relationship between noise level function (NLF) σI and inverse camera response function (CRF) g
We model NLF based on (7) for the following two considerations: 1) unlike the training based models [3], the parameters used here have physical meanings, and 2) estimation of NLF can be well constrained by the shape of CRF, especially in the relatively low noise level conditions
Summary
Random noise is mainly caused by quantum effects, thermal fluctuations and dark current leakage. The AWGN conjecture may not hold for real-life digital photographs because the actual CMOS/CCD sensor noise is strongly dependent on the light intensity, and the forgery makers are unlikely to deliberately add noise to lower the visual quality of the fake images. Based on this consideration, Liu et al [3] define a noise level function (NLF) with respect to image intensity. Liu et al [3] define a noise level function (NLF) with respect to image intensity They collect a sample set representing spatial average and variation, and find the lower envelope of the samples.
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