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
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.

通过构建一个包含具有综合性的不同自然场景下的真实噪声图像的新基准数据集，并在其上评估了各种图像去噪方法（包括之前的数据集），证明近期针对稀疏或低秩理论设计的方法实现了更好的去噪效果，更为稳健，并且新提出的数据集更具挑战性。该数据集现已公开，供研究人员研究新的现实世界图像去噪方法。