Brief review of image denoising techniques

• Published in: Visual computing for industry, biomedicine and art Vol. 2; no. 1; pp. 7 - 12
• Main Authors: Fan, Linwei, Zhang, Fan, Fan, Hui, Zhang, Caiming
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 08.07.2019; Springer Nature B.V; SpringerOpen
• Subjects: CAE) and Design; Computer Graphics; Computer Imaging; Computer Science; Computer-Aided Engineering (CAD; Convolutional neural network; Digital imaging; Image denoising; Image Processing and Computer Vision; Image quality; Low-rank; Media Design; Noise; Noise reduction; Non-local means; Pattern Recognition and Graphics; Review; Sparse representation; Vision; Low-rank; Sparse representation; Non-local means; Convolutional neural network; Image denoising
• ISSN: 2524-4442; 2096-496X; 2524-4442
• DOI: 10.1186/s42492-019-0016-7

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.