Image Noise Reduction Techniques Using Machine Learning

Abstract

Image noise is a ubiquitous problem of digital imaging which reduces the quality of visual representation and affects the functionality of computer vision systems. Noise may exist due to a number of factors, such as sensor flaws, low-light processing, and transmission noise. The conventional methods of noise reduction, including filtering and transform-domain methods, tend to be non-restrictive to the details of the image, at the expense of noise reduction. Over the last few years, machine learning (ML) and deep learning systems have become useful tools in image denoising, where data-driven models can be used to restore data to a high quality. This paper evaluates and compares different noise reduction algorithms based on machine learning, such as supervised and unsupervised algorithms, and hybrid models with respect to their effectiveness, computational cost, and ability to address various types of noise. Other issues covered in the study are overfitting, generalization, and performance in practical application. The results show that deep convolutional neural networks (CNNs), autoencoders, and generative adversarial networks (GANs) are better at noise suppression and structural details conservation. The application of the ML-based denoising to the imaging sequence has shown great potential in the medical imaging, remote sensing, and surveillance fields, and further research is necessary in this field.