Real-world Noisy Image Denoising: A New Benchmark

TL;DR

This paper introduces a new benchmark dataset for real-world noisy image denoising, highlighting the limitations of previous methods focused on synthetic noise and demonstrating the effectiveness of specialized denoising techniques on real data.

Contribution

The paper presents a comprehensive real-world noisy image dataset captured with various cameras, and evaluates existing denoising methods, emphasizing the need for realistic benchmarks.

Findings

Methods based on sparse or low-rank theories perform better on real noise.

The new dataset is more challenging than previous datasets.

Specialized denoising methods show improved robustness.

Abstract

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.