  Most of previous image denoising methods focus on additive white Gaussian
noise (AWGN). However,the real-world noisy image denoising problem with the
advancing of the computer vision techiniques. In order to promote the study on
this problem while implementing the concurrent real-world image denoising
datasets, we construct a new benchmark dataset which contains comprehensive
real-world noisy images of different natural scenes. These images are captured
by different cameras under different camera settings. We evaluate the different
denoising methods on our new dataset as well as previous datasets. Extensive
experimental results demonstrate that the recently proposed methods designed
specifically for realistic noise removal based on sparse or low rank theories
achieve better denoising performance and are more robust than other competing
methods, and the newly proposed dataset is more challenging. The constructed
dataset of real photographs is publicly available at
\url{https://github.com/csjunxu/PolyUDataset} for researchers to investigate
new real-world image denoising methods. We will add more analysis on the noise
statistics in the real photographs of our new dataset in the next version of
this article.
