Adaptive Image Filtering Based on Convolutional Neural Network

Abstract

The process of digital image acquisition and transmission is easy to be polluted by noise. Noises can also cause disturbances, or even misjudgements in the remote sensing image, face recognition, image classification of machine learning and deep learning. Therefore the correctness and safety of image usage is greatly reduced. Different types of noise may occur under various conditions, and the same filtering method has different effects on different types of noise processing, which makes it difficult to select the best way to filtering the image. So the detection and recognition of noise type has always been a hot topic in the field of information security. However, there are lacking solutions to the current noise type identification problem, and the complexity is very high. In this paper, a convolutional neural network(CNN) model which is able to automatically identify salt and pepper noise, Gauss noise and random noise based on deep learning training is proposed. After that, median filter, mean filter and wiener filter are used to filter the corresponding images. The purpose of ensuring the correctness and security of the image application is achieved. By simulating the images of different noise and analyzing PSNR, it is proved that this method able to distinguish the noise and filter obviously.