Machine Learning for Image Denoising: A Review

Due to the increasing demand for high-quality images in various applications, more attention is paid to image denoising to improve image quality. However, the traditional image-denoising methods have great limitations for complex noise patterns. Machine learning (ML), especially deep learning (DL), has been given attention to solving this problem and has a good prospect for image denoising. This paper raises the necessity of image denoising first, then some common noise types are introduced, and finally, the technology of denoising— ML is discribed. The combined use of image-denoising methods can figure out complex situations to a large extent, providing a guarantee for high-quality images. Since DL can automatically learn complex patterns and levels from data, DL architectures such as CNNs and GANs are used to denoise. We can determine the quality of denoising results by evaluation metrics that provide quantitative measures, such as PSNR and MSE. This paper comprehensively considers the effectiveness of ML and DL in image denoising, and affirms its future potential.