  Image denoising algorithms are evaluated using images corrupted by artificial
noise, which may lead to incorrect conclusions about their performances on real
noise. In this paper we introduce a dataset of color images corrupted by
natural noise due to low-light conditions, together with spatially and
intensity-aligned low noise images of the same scenes. We also introduce a
method for estimating the true noise level in our images, since even the low
noise images contain small amounts of noise. We evaluate the accuracy of our
noise estimation method on real and artificial noise, and investigate the
Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six
denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer
Perceptron, and two versions of NL-means. We show that while the Multi-Layer
Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on
gray images with synthetic noise, they lag behind on our dataset.
