General image reconstruction algorithm based on deep learning and compressed sensing theory
As an efficient carrier of information,digital images play an increasingly important role in information transmission.With the increasing of image data,compressed sensing technology is needed to solve the cost waste and time in the process of data storage and transmission.Traditional compressed sensing operation takes a long time to reconstruct and has poor reconstruction quality,so it cannot be recovered at low sampling rate.In this paper,an image reconstruction algorithm based on deep learning compressed sensing theory is proposed,which is suitable for both grayscale and color images.The compression reconstruction network uses bilinear interpolation to compress the width and height of the image,and the lost information is learned by the fully connected layer.The full connection layer is used many times to construct the network,so that it has more network parameter learning image features.For color images,the 3 channels are compressed into 1 channel by convolutional neural network.Finally,the reconstruction network uses bilinear interpolation to enlarge the compressed image,and the convolutional neural network and the fully connected layer are used to reconstruct the high-quality image.Experiments show that under different sampling rates,the proposed CCSNet network has the optimal PSNR and SSIM values,and the reconstruction performance is better than the deep learn-based ReconNet,DR2-Net and MSRNet networks.The algorithm is suitable for both grayscale image and RGB format color image,and has great advantages in improving reconstruction quality and shortening reconstruction time while keeping the running time as short as possible.
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