Image Quality Improvement of Surveillance Camera Images by Learning-based Denoising Method Utilizing Noise2Noise

Abstract

In recent years, the number of surveillance cameras installed has increased. Surveillance cameras need to be able to capture images even under poor shooting conditions such as low exposure. However, noise may be generated in the captured images under such environments. Although there have been many studies on image denoising, most of them target only synthetic noise such as Gaussian noise or real image noise such as the SIDD dataset and have not demonstrated sufficient performance for captured images. In this paper, we investigate the construction of an effective CNN model for real image noise using Noise2Noise. In addition, Noise2Noise has the problem of significantly degraded performance compared to normal learning when data is small. Therefore, we propose a learning method that can build models with good performance even when data is small, by pre-training with an open dataset such as SIDD and then re-training with Noise2Noise.