Image noise recognition algorithm based on data enhancement
Lei Guan, Jijiang Huang, Hao Wang, Weining Chen, Rong Lu, Zhiqiang Chen · 2022
Image denoising has always been an indispensable part of image processing tasks, and the quality of image preprocessing algorithms is directly related to the effect of subsequent image processing. Most existing denoising algorithms explicitly define the type and distribution of noise, for example, the artificially set additive Gaussian white noise which is based on prior knowledge. But on the one hand, the actual real image noise is not equivalent to Gaussian noise. The noise distribution of different noise types such as Gaussian, Salt and pepper noise, Rayleigh, Uniform noise is very different. It is not possible to generalize in the process of image denoising. On the other hand, images in real scenarios often have problems such as unrepeatable acquisition, lack of data information, or even only single image information, which can't support large-scale deep learning and discrimination. The above two points limit the application of most algorithms in real scenarios.