New image denoising algorithm via improved deep convolutional neural network with perceptive loss

Abstract

In order to remove the noise effectively and obtain the high quality denoised image, the new image denoising algorithm is proposed based on improved convolutional neural network with joint loss function (MP-DCNN). Firstly, the end to end adaptive residual convolutional neural network is constructed by using adaptive convolutional and residual learning for modeling the noisy image. Secondly, the input noise image features are extracted by adaptive convolution and leaky rectified linear units (Leaky ReLU) active function in the first half of the network, and then the image features reconstruction is performed by the other structure and convolutional operation of the network. Finally, the initial denoised image is obtained using the mean square error (MSE) loss function which can learn and optimize the extracted feature. Meanwhile, the initial denoising image is input to the pre-trained SegNet which can obtain a better learn of the image edge information. The final denoised image is obtained by using the perceptive loss function. To demonstrate the effectiveness of the proposed image denoising algorithm, the performance of the proposed algorithm is experimentally verified on a variety of noise levels. The experimental results show that the proposed algorithm can obtain superior performance both in visual quality and two evaluation indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), compared with other state-of-the-art denoising algorithms. Especially for high noisy environment with a noise standard deviation of 60, the algorithm finally obtains 24.26 dB of average PSNR in the test dataset. Its average PSNR is about 0.5 dB and 1 dB higher than DnCNN and BM3D, respectively. At the same time, the proposed method obtain denoising image with no boundary artifact finally. The proposed algorithm can significantly improve the performance of the image denoising system from the perspective of expert and intelligent systems.