Research Area: Machine Learning
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.
Keywords:
Image denoising
Non-local means
Sparse representation
Low-rank
Convolutional neural network
Deep Learning
Author(s) Name: Linwei Fan, Fan Zhang, Hui Fan & Caiming Zhang
Journal name: Visual Computing for Industry, Biomedicine, and Art
Conferrence name:
Publisher name: Springer
DOI: 10.1186/s42492-019-0016-7
Volume Information: volume 2, Article number: 7 (2019)
