The classification and denoising of image noise based on deep neural networks

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 50; no. 7; pp. 2194 - 2207
• Main Authors: Liu, Fan, Song, Qingzeng, Jin, Guanghao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computer Science; Computer vision; Image classification; Machine learning; Machines; Manufacturing; Mechanical Engineering; Neural networks; Noise; Noise reduction; Processes; Random noise; Deep learning; PSNR; SSIM; Classification; Image denoising
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-019-01623-0

Currently, image denoising is a challenge in many applications of computer vision. The existing denoising methods depend on the information of noise types or levels, which are generally classified by experts. These methods have not applied computational methods to pre-classify the image noise types. Furthermore, some methods assume that the noise type of the image is a certain one like Gaussian noise, which limits the ability of the denoising in real applications. Different from the existing methods, this paper introduces a new method that can classify and denoise not only a certain type noise but also mixed types of noises for real demand. Our method utilizes two types of deep learning networks. One is used to classify the noise type of the images and the other one performs denoising based on the classification result of the first one. Our framework can automatically denoise single or mixed types of noises with these efforts. Our experimental results show that our classification network achieves higher accuracy, and our denoising network can ensure higher PSNR and SSIM values than the existing methods.