Image Denoising Algorithm Based On Convolutional Neural Network

With the popularity of various digital instruments and digital products,images and video have become the most commonly used information carriers in human life.The image contains a lot of information in our daily life and becomes the main way for people to get the original information from the outside world.While in the process of acquisition,transmission and preservation of image singnal it will inevitably brings in various noises which can degrading the image.This noise not only affects people's visual experience,but also greatly affects subsequent image processing,such as medical imaging,satellite imaging,and so on.Image denoising is still very challenging in image processing.Many researchers are thinking about how to remove noise more thoroughly and preserve the original image information to the utmost extent?This paper mainly studies the methods of image denoising based on convolutional neural networks.The main contributions of the thesis includes:?1?A denoising model based on deep convolutional neural network is proposed.In order to extract the deeper features of the image and solve the problem of poor learning efficiency caused by the increase of network depth,a residual network is introduced.Most of the denoising methods are to train different models to deal with different intensities of noise,so you need to estimate the noise intensity first,and then use the corresponding model to denoise.We used a trained MLP^[1]model to test"Lena"with a standard deviation?of 10,25,50 and 70 Gaussian white noise the corresponding PSNR is 28.87 dB,31.28 dB,17.66 dB and 13.82dB respectively.From this result we can get that the model trained at a single noise intensity can not handle other intensity noise very well.A convolutional neural network model designed in this paper and it can handle different intensities of noise.?2?We have combined the denoising model with stationary wavelet transform in order to better preserve the original image information while denoising.Many current deep learning image denoising methods exploit the strong nonlinear fitting ability of convolutional neural networks to learn pixel-level mapping from noisy images to clean images,but sometimes they will produce smoother results when dealing with high-intensity noise,and will lose some texture details of the original image.In order to improve the performance under high-level noise,this paper proposes an image denoising method which combined with neural network and stationary wavelet transform.Instead of directly predicting the pixels of the clean image,we first predict the wavelet coefficients of the clean image and then reconstruct the denoised image.The denoising model used in this paper is composed of four parts:stationary wavelet transform,feature extraction network,wavelet coefficients prediction network and the reconstruction of denoised image.We directly take noisy image as input,after the stationary wavelet transform,different subband coefficients are generated through correspoding subnetwork.The coefficients prediction network which composed of convolutional neural networks is used to extract the features of each subbands and predict the subband coefficients of the clean images.The experimental results show that the PSNR and SSIM of our method can basically approach the current excellent denoising algorithm in the low-intensity noise.When the gaussian noise's standard deviation is higher than 25,we can surpass those excellent algorithm and can achieve good visual effects.