[2018/02] 3D ConvNet classifier for Alzheimer is now published. H. Choi and K. H. Jin, "Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging," Behavioural Brain Research, vol 344, (2018), pp 103-109 This work was also featured by MIT technology review (Apr. 28.2017) link[2017/06] K. H. Jin, M.T. McCann, E. Froustey, M. Unser , "Deep Convolutional Neural Network for Inverse Problems in Imaging," IEEE Transactions on Image Processing, 26.9 (2017): 4509-4522. [The most frequently accessed document in IEEE TIP 2017.10~12, 2018.01]Code (matlab, tensorflow) is open [github] [2018/09] K. H. Jin, G. Kim, Y. Leblebici, J. C. Ye, M. Unser, "Direct Reconstruction of Saturated Samples in Band-Limited OFDM Signals," arXiv Summary : non-iterative interpolation using bandlimitedness [2018/06] J. Min, K. H. Jin, M. Unser, and J. C. Ye, "Grid-free localization algorithm using low rank Hankel matrix for super-resolution microscopy," is now accepted in IEEE Trans. on Image Processing [2018/06] H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser, "CNN-Based projected gradient descent for consistent CT image reconstruction," is now published in IEEE Trans. on Medical Imaging [2018/06] K. Lee, Y. Li, K.H. Jin, and J. C. Ye, "Unified theory for recovery of sparse signals in a general transform domain," is now accepted in IEEE Trans. on Information Theory [2017/11] A Review article for inverse problem with CNN is now appeared. M. T. McCann, K. H. Jin, and M. Unser. "Convolutional Neural Networks for Inverse Problems in Imaging: A Review." IEEE Signal Processing Magazine 34.6 (2017): 85-95.[2017/11] K. H. Jin, and J. C. Ye. "Sparse and Low Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal," IEEE Trans. on Image Processing. vol. 27, no. 3, pp. 1448-1461, March 2018.Code (matlab) is open [github] [2017] K. H. Jin, D. Lee, and J. C. Ye, "A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix," IEEE Transactions on Computational Imaging, vol. 2, no. 4, pp. 480-495, Dec. 2016.K. H. Jin, M.T. McCann, E. Froustey, M. Unser , "Deep Convolutional Neural Network for Inverse Problems in Imaging." arXiv preprint arXiv:1611.03679 (2016).[2015] ALOHA paper for image inpainting is just published on IEEE Transactions on Image Processing (full title : Annihilating filter based low rank Hankel matrix approach for image inpainting) Application : Image Inpainting on randomly sampled image restoration, object removal, RGB inpainting, etc. [2012] [optics express] High-speed THz reflection tomography - floppy disk & GFRP sample |

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