Research On Image Noise Recognition And Removal Technology Based On Deep Learning

Currently,image denoising is still a hot research topic which faces enormous challenges in the field of computer vision research and application.Most of the existing denoising methods depend on the type of noise that the image contains,and this information is mainly manually labeled by experienced personnel.In other words,these methods do not apply computational methods to automatically pre-classify image noise types,which undoubtedly take a lot of time and labor.In addition,the previous methods mostly assume that the image only contains some single noise type like Gaussian noise,which is not in line with the actual application scenario.In the actual scene,the image may contain a variety of noises,and the previous method will not effectively denoise the image.Different from the existing methods,this paper introduces a novel method,which can not only perform noise classification and image denoising on a single type of noise image,but also can classify and denoise noise images containing mixed types according to actual scene requirements.Our method mainly utilizes two types of deep learning networks to classify and denoise noise images,namely noise classification and recognition networks and noise removal networks.The noise classification network is used to identify and classify the type of noise contained in the image,and the noise removal network is used to select a suitable denoising model to remove the image noise according to the type of image noise recognized by the noise classification network,thereby obtaining a clean and clear image.The framework designed in this paper can automatically denoise images containing different noise types through the above two network modules.The final experimental results show that the classification network designed in this paper achieves excellent recognition accuracy of more than 90% for the noise categories contained in the image.In addition,the designed denoising network also obtains higher peak signal-to-noise ratio and structural similarity evaluation values than the previous methods in Gaussian noise removal,and also has good denoising effects on other single type and mixed type noise.