The application of machine learning algorithms in image processing denoising
Di Lu, Zhengqiang Xiong · 2024
In order to solve the problem of losing the original information of images in traditional image processing methods during denoising, this paper proposes an application method of machine learning algorithm in image processing denoising. This also means that it is necessary to continuously compare the processed image with the original image, which will increase the time required for denoising. However, deep learning techniques have played an important role in this regard. Through intelligent technology, especially supervised machine learning algorithms, information images can be better processed, enabling people to observe things more clearly and take correct actions. Specifically, we can use supervised machine learning algorithms to extract noise during image denoising. This method is based on setting separation nodes for image noise at multiple points, and then using clustering theory to filter out noise, thereby completing image denoising. The experimental results show that using animal images as the test object, we can detect the noise contained in them, and by comparing the effects of the original method and the new method, we found that the new method can obtain results consistent with the original image. More importantly, without introducing image distortion, the new method controls the denoising time to 1.15 seconds, while the original method takes an average of 20.21 to 15.13 seconds. This indicates that the new method can significantly improve the efficiency of denoising and has the potential for practical applications.