A novel super-resolution CT image reconstruction via semi-supervised generative adversarial network

Reconstruction of super-resolution CT images using deep learning requires a large number of high-resolution images. However, high-resolution images are often limited to access due to CT performance and operation factors. In this paper, a new semi-supervised generative adversarial network is presented to accurately recover high-resolution CT images from low-resolution counterparts. We use a deep unsupervised network of 16 residual blocks to design the generator and build a discriminator based on a supervised network. We also apply a parallel 1 × 1 convolution operation to reduce the dimensionality of each hidden layer’s output. Four types of loss functions are presented to build a new one for enforcing the mappings between the generator and discriminator. The bulk specification layer in the commonly used residual network is removed to construct a new type of residual network. In terms of experiments, we conduct an objective and subjective comprehensive evaluation with several state-of-the-art methods. The comparison results show that our proposed network has better advantages in super-resolution image reconstruction.

[1]  Thomas S. Huang,et al.  Computed tomography super-resolution using convolutional neural networks , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[2]  Dayong Wang,et al.  Single assignment based nearest neighbor interpolation algorithm for digital holographic diffraction tomography , 2018, SPIE/COS Photonics Asia.

[3]  Nikolas P. Galatsanos,et al.  Maximum a posteriori super-resolution of compressed video using a new multichannel image prior , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Takio Kurita,et al.  Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network , 2017, ICONIP.

[5]  周靖鸿 Zhou Jinghong,et al.  A Method of Super-Resolution Reconstruction for Remote Sensing Image Based on Non-Subsampled Contourlet Transform , 2015 .

[6]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[7]  André Kaup,et al.  Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[8]  Masoumeh Zareapoor,et al.  Perceptual image quality using dual generative adversarial network , 2020, Neural Computing and Applications.

[9]  Jie Yang,et al.  Diverse adversarial network for image super-resolution , 2019, Signal Process. Image Commun..

[10]  Xin Jiang,et al.  Particle swarm optimization with damping factor and cooperative mechanism , 2019, Appl. Soft Comput..

[11]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[12]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[13]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[14]  Xuyun Zhang,et al.  A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data , 2017, IEEE Journal on Selected Areas in Communications.

[15]  Harry Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Heng Su,et al.  Survey of Super-resolution Image Reconstruction Methods: Survey of Super-resolution Image Reconstruction Methods , 2014 .

[17]  Zhang Wei,et al.  Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches , 2012 .

[18]  Jing Li,et al.  Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution. , 2018, Physics in medicine and biology.

[19]  Michal Irani,et al.  Super resolution from image sequences , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[20]  Guang Li,et al.  CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) , 2018, IEEE Transactions on Medical Imaging.

[21]  Siyuan Liu,et al.  Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[22]  Jiguo Yu,et al.  Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering , 2017, IEICE Trans. Inf. Syst..

[23]  R. Keys.  Cubic convolution interpolation for digital image processing , 1981 .

[24]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[25]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[26]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[27]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[28]  Heung-Yeung Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004 .

[29]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[30]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

[31]  Jinjun Chen,et al.  Weighted principal component analysis-based service selection method for multimedia services in cloud , 2014, Computing.

[32]  Zhen Wang,et al.  On the Effectiveness of Least Squares Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[34]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[35]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[36]  Ge Wang,et al.  Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising , 2018, IEEE Access.

[37]  Chao Chen,et al.  Deep joint two-stream Wasserstein auto-encoder and selective attention alignment for unsupervised domain adaptation , 2019, Neural Computing and Applications.

[38]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Su Heng,et al.  Survey of Super-resolution Image Reconstruction Methods , 2013 .

[40]  Qiang He,et al.  Time-aware distributed service recommendation with privacy-preservation , 2019, Inf. Sci..

[41]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Debiao Li,et al.  Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network , 2018, MICCAI.

[43]  Jiguo Yu,et al.  An Invocation Cost Optimization Method for Web Services in Cloud Environment , 2017, Sci. Program..

[44]  Jinjun Chen,et al.  A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment , 2018, Future Gener. Comput. Syst..

[45]  Lianyong Qi,et al.  Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment , 2018, Wirel. Commun. Mob. Comput..

[46]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[47]  Mingzhe Liu,et al.  A new local density and relative distance based spectrum clustering , 2019, Knowledge and Information Systems.

[48]  Jeff Wood,et al.  Super‐resolution musculoskeletal MRI using deep learning , 2018, Magnetic resonance in medicine.

[49]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.