A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

A. Velten, R. Raskar, and M. Bawendi, “Picosecond camera for time-of-flight imaging,” in “Imaging and Applied Optics,” (Optical Society of America, 2011), p. IMB4.

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

H. Bristow, A. Eriksson, and S. Lucey, “Fast convolutional sparse coding,” in “Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on,” (IEEE, 2013), pp. 391–398.

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Comm. Pure Appl. Math. 59, 1207–1223 (2006).

[Crossref]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

J. Lin, Y. Liu, M. B. Hullin, and Q. Dai, “Fourier analysis on transient imaging with a multifrequency time-of-flight camera,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

A. Kirmani, T. Hutchison, J. Davis, and R. Raskar, “Looking around the corner using transient imaging,” in “Computer Vision, 2009 IEEE 12th International Conference on,” (IEEE, 2009), pp. 159–166.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory 52, 1289–1306 (2006).

[Crossref]

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).

[Crossref]

H. Bristow, A. Eriksson, and S. Lucey, “Fast convolutional sparse coding,” in “Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on,” (IEEE, 2013), pp. 391–398.

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in “Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on,” (IEEE, 2010), pp. 2528–2535.

M. D. Zeiler and R. Fergus, “Learning image decompositions with hierarchical sparse coding,” Tech. Rep. TR2010-935, Courant Institute of Mathematical Science, New York University (2010).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inform. Theory 53, 4655–4666 (2007).

[Crossref]

F. Heide, M. B. Hullin, J. Gregson, and W. Heidrich, “Low-budget transient imaging using photonic mixer devices,” ACM Trans. Graph. (Proc. SIGGRAPH 2013) 32, 45 (2013).

E. Grushka, “Characterization of exponentially modified gaussian peaks in chromatography,” Anal. Chem. 44, 1733–1738 (1972).

[Crossref]
[PubMed]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

M. Mørup, M. N. Schmidt, and L. K. Hansen, “Shift invariant sparse coding of image and music data,” Tech. rep., Technical University of Denmark, Richard Petersens Plads bld. 321, 2800 Kgs. Lyngby, Denmark (2008).

F. Heide, M. B. Hullin, J. Gregson, and W. Heidrich, “Low-budget transient imaging using photonic mixer devices,” ACM Trans. Graph. (Proc. SIGGRAPH 2013) 32, 45 (2013).

F. Heide, L. Xiao, W. Heidrich, and M. B. Hullin, “Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

F. Heide, M. B. Hullin, J. Gregson, and W. Heidrich, “Low-budget transient imaging using photonic mixer devices,” ACM Trans. Graph. (Proc. SIGGRAPH 2013) 32, 45 (2013).

F. Heide, L. Xiao, W. Heidrich, and M. B. Hullin, “Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in “Adv. Neural Inf. Process. Syst.”, (2012), pp. 1097–1105.

F. Heide, M. B. Hullin, J. Gregson, and W. Heidrich, “Low-budget transient imaging using photonic mixer devices,” ACM Trans. Graph. (Proc. SIGGRAPH 2013) 32, 45 (2013).

F. Heide, L. Xiao, W. Heidrich, and M. B. Hullin, “Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

J. Lin, Y. Liu, M. B. Hullin, and Q. Dai, “Fourier analysis on transient imaging with a multifrequency time-of-flight camera,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

A. Kirmani, T. Hutchison, J. Davis, and R. Raskar, “Looking around the corner using transient imaging,” in “Computer Vision, 2009 IEEE 12th International Conference on,” (IEEE, 2009), pp. 159–166.

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

A. Kirmani, T. Hutchison, J. Davis, and R. Raskar, “Looking around the corner using transient imaging,” in “Computer Vision, 2009 IEEE 12th International Conference on,” (IEEE, 2009), pp. 159–166.

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding 114, 1318–1328 (2010).

[Crossref]

M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding 114, 1318–1328 (2010).

[Crossref]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in “Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on,” (IEEE, 2010), pp. 2528–2535.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in “Adv. Neural Inf. Process. Syst.”, (2012), pp. 1097–1105.

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).

[Crossref]

R. Lange and P. Seitz, “Solid-state time-of-flight range camera,” IEEE J. Quantum Electron. 37, 390–397 (2001).

[Crossref]

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

M. S. Lewicki and T. J. Sejnowski, “Coding time-varying signals using sparse, shift-invariant representations,” in “Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II,” (MIT, Cambridge, MA, USA, 1999), pp. 730–736.

J. Lin, Y. Liu, M. B. Hullin, and Q. Dai, “Fourier analysis on transient imaging with a multifrequency time-of-flight camera,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding 114, 1318–1328 (2010).

[Crossref]

J. Lin, Y. Liu, M. B. Hullin, and Q. Dai, “Fourier analysis on transient imaging with a multifrequency time-of-flight camera,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

H. Bristow, A. Eriksson, and S. Lucey, “Fast convolutional sparse coding,” in “Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on,” (IEEE, 2013), pp. 391–398.

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

M. Mørup, M. N. Schmidt, and L. K. Hansen, “Shift invariant sparse coding of image and music data,” Tech. rep., Technical University of Denmark, Richard Petersens Plads bld. 321, 2800 Kgs. Lyngby, Denmark (2008).

P. Datte, A. M. Manuel, M. Eckart, M. Jackson, H. Khater, and M. Newton, “Evaluating radiation induced noise effects on pixelated sensors for the national ignition facility,” (2013), vol. 8850, pp. 885003.

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

S. Rangan, “Generalized approximate message passing for estimation with random linear mixing,” in “Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on,” (IEEE, 2011), pp. 2168–2172.

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

A. Velten, R. Raskar, and M. Bawendi, “Picosecond camera for time-of-flight imaging,” in “Imaging and Applied Optics,” (Optical Society of America, 2011), p. IMB4.

A. Kirmani, T. Hutchison, J. Davis, and R. Raskar, “Looking around the corner using transient imaging,” in “Computer Vision, 2009 IEEE 12th International Conference on,” (IEEE, 2009), pp. 159–166.

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Comm. Pure Appl. Math. 59, 1207–1223 (2006).

[Crossref]

M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding 114, 1318–1328 (2010).

[Crossref]

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

M. Mørup, M. N. Schmidt, and L. K. Hansen, “Shift invariant sparse coding of image and music data,” Tech. rep., Technical University of Denmark, Richard Petersens Plads bld. 321, 2800 Kgs. Lyngby, Denmark (2008).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

R. Lange and P. Seitz, “Solid-state time-of-flight range camera,” IEEE J. Quantum Electron. 37, 390–397 (2001).

[Crossref]

M. S. Lewicki and T. J. Sejnowski, “Coding time-varying signals using sparse, shift-invariant representations,” in “Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II,” (MIT, Cambridge, MA, USA, 1999), pp. 730–736.

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in “Adv. Neural Inf. Process. Syst.”, (2012), pp. 1097–1105.

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Comm. Pure Appl. Math. 59, 1207–1223 (2006).

[Crossref]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in “Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on,” (IEEE, 2010), pp. 2528–2535.

R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. R. Stat. Soc. Ser. B Stat. Methodol. pp. 267–288 (1996).

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inform. Theory 53, 4655–4666 (2007).

[Crossref]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

A. Velten, R. Raskar, and M. Bawendi, “Picosecond camera for time-of-flight imaging,” in “Imaging and Applied Optics,” (Optical Society of America, 2011), p. IMB4.

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
[PubMed]

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

F. Heide, L. Xiao, W. Heidrich, and M. B. Hullin, “Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in “Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on,” (IEEE, 2010), pp. 2528–2535.

M. D. Zeiler and R. Fergus, “Learning image decompositions with hierarchical sparse coding,” Tech. Rep. TR2010-935, Courant Institute of Mathematical Science, New York University (2010).

F. Heide, M. B. Hullin, J. Gregson, and W. Heidrich, “Low-budget transient imaging using photonic mixer devices,” ACM Trans. Graph. (Proc. SIGGRAPH 2013) 32, 45 (2013).

A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Trans. Graph. (Proc. SIGGRAPH Asia 2013) 32, 167 (2013).

E. Grushka, “Characterization of exponentially modified gaussian peaks in chromatography,” Anal. Chem. 44, 1733–1738 (1972).

[Crossref]
[PubMed]

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Comm. Pure Appl. Math. 59, 1207–1223 (2006).

[Crossref]

M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding 114, 1318–1328 (2010).

[Crossref]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3, 1–122 (2011).

[Crossref]

R. Lange and P. Seitz, “Solid-state time-of-flight range camera,” IEEE J. Quantum Electron. 37, 390–397 (2001).

[Crossref]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory 52, 1289–1306 (2006).

[Crossref]

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inform. Theory 53, 4655–4666 (2007).

[Crossref]

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).

[Crossref]
[PubMed]

A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Opt. Lett. 39, 1705–1708 (2014).

[Crossref]
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[Crossref]

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[Crossref]
[PubMed]

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).

[Crossref]

D. Freedman, E. Krupka, Y. Smolin, I. Leichter, and M. Schmidt, “SRA: Fast removal of general multipath for tof sensors,” arXiv preprint arXiv:1403.5919 (2014).

R. Schwarte, Z. Xu, H. Heinol, J. Olk, R. Klein, B. Buxbaum, H. Fischer, and J. Schulte, “New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device,” in “Proc. SPIE,”, vol. 3100 (1997), vol. 3100, pp. 245–253.

[Crossref]

M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Trans. Graph. (Proc. SIGGRAPH)33 (2014).

A. Velten, R. Raskar, and M. Bawendi, “Picosecond camera for time-of-flight imaging,” in “Imaging and Applied Optics,” (Optical Society of America, 2011), p. IMB4.

S. Rangan, “Generalized approximate message passing for estimation with random linear mixing,” in “Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on,” (IEEE, 2011), pp. 2168–2172.

F. Heide, L. Xiao, W. Heidrich, and M. B. Hullin, “Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors,” in “IEEE Conference on Computer Vision and Pattern Recognition (CVPR),” (2014).

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