1. K. Degraux, U. S. Kamilov, P. T. Boufounos, and D. Liu, “Online Convolutional Dictionary Learning for Multimodal Imaging,” arXiv:1706.04256 [cs.CV], June 2017.
  2. H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, “SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering,” arXiv:1705.04281 [cs.CV], May 2017.

Notable Publications

  1. U. S. Kamilov and P. T. Boufounos, “Motion-Adaptive Depth Superresolution,” IEEE Trans. Image Process, vol. 26, no. 4, pp. 1723-1731, April 2017.
  2. U. S. Kamilov, “A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization,” IEEE Trans. Image Process., vol. 26, no. 2, pp. 539-548, February 2017.
  3. U. S. Kamilov and H. Mansour, “Learning optimal nonlinearities for iterative thresholding algorithms,” IEEE Signal Process. Letters, vol. 23, no. 5, pp. 747–751, May 2016.
    [doi:10.1109/lsp.2016.2548245] [arXiv:1512.04754]
  4. U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imag., vol. 2, no. 1, pp. 59–70, March 2016.
  5. U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, 
”Learning Approach to Optical Tomography,” Optica, vol. 2, no. 6, pp. 517–522, June 2015.
    [doi:10.1364/optica.2.000517] [Nature “News and Views”]
  6. U. S. Kamilov, S. Rangan, A. K. Fletcher, and M. Unser, “Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning,” Proc. Ann. Conf. Neural Information Processing Systems (NIPS 2012) (Lake Tahoe, Nevada, December 3-6), pp. 2447-2455.
  7. U. S. Kamilov, V. K. Goyal, and S. Rangan, “Message-Passing De-Quantization with Applications to Compressed Sensing,” IEEE Trans. Signal Process., vol. 60, no. 12, pp. 6270–6281, December 2012.
    [doi:10.1109/tsp.2012.2217334] [arXiv:1105.6368]
  8. U. S. Kamilov, E. Bostan, and M. Unser, “Wavelet Shrinkage with Consistent Cycle Spinning Generalizes Total Variation Denoising,” IEEE Signal Process. Letters, vol. 19, no. 4, pp. 187–190, April 2012.