The image super-resolution reconstruction methods based on convolutional neural network mostly assume that a low resolution(LR)image is obtained by Bicubic downsampling of high resolution(HR)image.However,LR images in real environment contain unknown noise,which inevitably leads to poor network performance.To solve this common problem,A noise-resistant multistep image super resolution network is proposed.First of all,combine information distilling image denoising network and generative adversarial network to train the denoising network,in order to improve image denoising ability of the network;Secondly,the pure feature map in the middle layer of the network and the denoised image are combined with the stepwise image super-resolution reconstruction network,which is combined with the stepwise network training,to reconstruct low-resolution image of the real environment effectively.The proposed network is trained and evaluated on BSD100* and BSD100#datasets with gaussian noise.Experimental results show that the proposed network achieves good improvement in image quality evaluation and visual comparison compared with existing advanced networks.
关键词
深度学习/图像降噪网络/图像超分辨率重构网络/生成对抗网络/分步式网络训练
Key words
deep learning/image denoising network/image super-resolution reconstruction network/generative adversarial network/stepwise network training
维普
万方数据