Optimized deep learning algorithms for application with data from PMDcameras

References [1] C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep Convolutional Networks”, in IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (2), (2014) [2] Y. Tai, J. Yang, and X. Liu, “Image Super-Resolution via Deep Recursive Residual Network”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017) [3] J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution”, in 14th European Conference on Computer Vision (ECCV), (2016) [4] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunnignham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017) [5] B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced Deep Residual Networks for Single Image Super-Resolution”, in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2017) [6] M.S.M. Sajjadi, B. Schölkopf, and M. Hirsch, „EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis” in International Conference on Computer Vision, (2017) [7] M. Bevilacqua, A. Roumy, C. Guillemot, and M.L. Alberi-Morel, “Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding”, in British Machine Vision Conference (BMVC), (2012) [8] J. Diebel, and S. Thrun, “An Application of Markov Random Fields to Range Sensing”, in Proceedings of Conference on Neural Information Processing Systems (NIPS), (2005) [9] J. Park, H. Kim, Y.W. Tai, M.S. Brown, and I. Kweon, “High Quality Depth Map Upsampling for 3D-TOF Cameras”, in International Conference on Computer Vision (ICCV), (2011) [10] D. Ferstl, C. Reinbacher, R. Ranftl, M. Rüther, and H. Bischof, “Image Guided Depth Upsampling using Anisotropic Total Generalized Variation”, in IEEE International Conference on Computer Vision (ICCV), (2013) [11] C. Jung, S. Yu, and J. Kim, “Intensity-guided edge-preserving depth upsampling through weighted L0 gradient minimization”, in J. Vis. Commun. Image R. 42, (2017) [12] H. Hirschmüller, and D. Scharstein, “Evaluation of Cost Functions for Stereo Matching”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2007) [13] D. Scharstein, and C. Pal, “Learning Conditional Random Fields for Stereo”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2007) [14] G. Riegler, D. Ferstl, M. Rüther, and H. Bischof, “A Deep Primal-Dual Network for Guided Depth Super-Resolution”, in Proceedings of the British Machine Vision Conference (BMVC), (2016) [15] X. Song, Y. Dai, and X. Qin, “Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network”, in 13th Asian Conference on Computer Vision (ACCV), (2016) [16] T.W. Hui, C.C. Loy, and X. Tang, “Depth Map Super-Resolution by Deep Multi-Scale Guidance”, in 14th European Conference on Computer Vision (ECCV), (2016) [17] F. van den Bergh, “Deferred slanted-edge analysis: a unified approach to spatial frequency response measurement on distorted images and color filter array subsets”, in Journal of the Optical Society of America A 35 (3), (2018)