基于改进CNN的压缩感知自然图像重建方法

Compressed sensing natural image reconstruction method based on improved CNN

传统压缩感知图像重建方法存在着运算量大、重建图像过程耗时以及低采样率下重建图像精度低等问题。针对这些问题,提出一种基于改进CNN的压缩感知自然图像重建方法,该方法主要包括:线性映射网络、初始重建网络和最终重建网络。首先,利用线性映射卷积网络自适应获得测量向量;其次,把通过学习得来的自适应测量向量输入到初始重建子像素卷积网络中,利用子像素卷积网络对上一步得到的特征图进行低分辨率初始重建;最后,利用残差网络对重建图像进行高精度重建,从而有效提升重建图像的质量。大量实验结果表明,该方法在PSNR、SSIM和视觉效果这三方面都明显优于其他方法,在采样率为0.0625时它的PSNR和SSIM的平均结果分别为31.55 dB和0.9253。

Traditional compressed sensing image reconstruction methods have many problems,such as large amount of computation,time-consuming reconstruction process and low accuracy reconstruction under low sampling rate.To solve these problems,a compressed sensing reconstruction method of natural image based on improved CNN is proposed,which mainly includes:Linear mapping network,initial and final reconstruction network reconstruction it first using linear mapping convolution network adaptive gain measurement vector,secondly,through the study of adaptive measurement vector input to the original reconstruction convolution sub pixel in the network,using the sub pixel characteristic figure obtained by convolution network to step on low resolution initial reconstruction,in the end,The residual network is used to reconstruct the reconstructed image with high precision so as to improve the quality of the reconstructed image effectively.A large number of experimental results show that this method is significantly superior to other methods in PSNR,SSIM and visual effect.When the sampling rate is 0.0625,the average results of PSNR and SSIM are 31.55 dB and 0.9253,respectively.