An Autocorrelation Function-Based Method Used for Image Denoising with Accuracy and Efficiency

Noise is always inevitably appeared in images, which directly affects the performance of machine vision applications. Currently, the commonly used denoising methods can be divided into three strategies: the filtering-based, model-based, and deep learning-based methods. However, they are always difficult to get the considerable accuracy and efficiency simultaneously. In this study, a novel denoising method based on autocorrelation function is investigated, which improve the image quality by utilizing the independence of useful periodic information and noise. Simulations and experiments compared with the current denoising methods confirm that the investigated method has a good comprehensive effect on noise reduction and efficiency improvement.