Real-world Noisy Image Denoising: A New Benchmark
This addresses the gap in image denoising research, which has focused on synthetic noise, by providing a dataset for real-world applications, though it is incremental as it builds on existing methods.
The authors tackled the problem of real-world noisy image denoising by constructing a new benchmark dataset with comprehensive real-world noisy images from various cameras and settings, and found that methods based on sparse or low-rank theories performed better and more robustly than others on this challenging dataset.
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.