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Phase retrieval algorithms have become an important component in many modern computational imaging systems.
A practical algorithm for the determination of phase from image and diffraction plane pictures
Gerchberg, R · 1972
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Reconstruction of an object from the modulus of its fourier transform
Fienup, J · 1978
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Phase retrieval algorithms: a comparison
Fienup, J · 1982
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Signal estimation from modified short-time fourier transform
Griffin, D. and Lim, J · 1984
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Phase retrieval and image reconstruction for astronomy
Dainty, C. and Fienup, J · 1987
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Imaging correlography with sparse arrays of detectors
Fienup, J. and Idell, P · 1988
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Phase retrieval in crystallography and optics
Millane, R · 1990
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
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Introduction to Fourier optics
Goodman, J · 2005
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Three-dimensional mapping of a deformation field inside a nanocrystal
Pfeifer, M., Williams, G., Vartanyants, I., Harder, R., and Robinson, I · 2006
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Phase retrieval via wirtinger flow: Theory and algorithms
Candes, E., Li, X., and Soltanolkotabi, M · 2007
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Image denoising by sparse 3-d transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Compressive phase retrieval
Moravec, M. L., Romberg, J. K., and Baraniuk, R. G · 2007
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Message-passing algorithms for compressed sensing
Donoho, D., Maleki, A., and Montanari, A · 2009
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Spatially adaptive filtering as regularization in inverse imaging: Compressive sensing super-resolution and upsampling
Danielyan, A., Foi, A., Katkovnik, V., Egiazarian, K., and Milanfar, P · 2010
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Learning fast approximations of sparse coding
Gregor, K. and LeCun, Y · 2010
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Generalized approximate message passing for estimation with random linear mixing
Rangan, S · 2011
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Phase retrieval via spatial light modulator phase modulation in 4f optical setup: numerical inverse imaging with sparse regularization for phase and amplitude
Katkovnik, V. and Astola, J · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
Cited alongside, same era.
Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming
Candes, E., Strohmer, T., and Voroninski, V · 2013
Cited alongside, same era.
Oversampling smoothness: an effective algorithm for phase retrieval of noisy diffraction intensities
Rodriguez, J., Xu, R., Chen, C., Zou, Y., and Miao, J · 2013
Cited alongside, same era.
Plug-and-play priors for model based reconstruction
Venkatakrishnan, S., Bouman, C., and Wohlberg, B · 2013
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Proximal: Efficient image optimization using proximal algorithms
Heide, F., Diamond, S., Nießner, M., Ragan-Kelley, J., Heidrich, W., and Wetzstein, G · 2016
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BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising
Metzler, C., Maleki, A., and Baraniuk, R · 2016
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Denoising based vector approximate message passing
Schniter, P., Rangan, S., and Fletcher, A · 2016
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Dolphin-dictionary learning for phase retrieval
Tillmann, A., Eldar, Y., and Mairal, J · 2016
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Phase retrieval meets statistical learning theory: A flexible convex relaxation
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Wide-field, high-resolution fourier ptychographic microscopy
Zheng, G.and Horstmeyer, R. and Yang, C · 2013
Cited alongside, same era.
Learning a deep convolutional network for image super-resolution
Dong, C., Loy, C., He, K., and Tang, X · 2014
Cited alongside, same era.
A field guide to forward-backward splitting with a fasta implementation
Goldstein, T., Studer, C., and Baraniuk, R · 2014
Cited alongside, same era.
Flexisp: A flexible camera image processing framework
Heide, F., Steinberger, M., Tsai, Y., Rouf, M., Pajak, D., Reddy, D., Gallo, O., Liu, J., Heidrich, W., Egiazarian, K., et al · 2014
Cited alongside, same era.
Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations
Katz, O., Heidmann, P., Fink, M., and Gigan, S · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
Cited alongside, same era.
Solving random quadratic systems of equations is nearly as easy as solving linear systems
Chen, Y. and Candes, E · 2015
Cited alongside, same era.
Bahmani, S. and Romberg, J · 2017
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Plug-and-play admm for image restoration: Fixed-point convergence and applications
Chan, S. H., Wang, X., and Elgendy, O. A · 2017
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Phasepack: A phase retrieval library
Chandra, R., Studer, C., and Goldstein, T · 2017
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One network to solve them all; solving linear inverse problems using deep projection models
Chang, J., Li, C., Póczos, B., Kumar, B., and Sankaranarayanan, A · 2017
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Unrolled optimization with deep priors
Diamond, S., Sitzmann, V., Heide, F., and Wetzstein, G · 2017
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Ptychnet: CNN based fourier ptychography
Kappeler, A., Ghosh, S., Holloway, J., Cossairt, O., and Katsaggelos, A · 2017
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Phase retrieval from noisy data based on sparse approximation of object phase and amplitude
Katkovnik, V · 2017
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Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset
Metzler, C., Sharma, M., Nagesh, S., Baraniuk, R., Cossairt, O., and Veeraraghavan, A · 2017
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Phase recovery and holographic image reconstruction using deep learning in neural networks
Rivenson, Y., Zhang, Y., Gunaydin, H., Teng, D., and Ozcan, A · 2017
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The little engine that could: Regularization by denoising (red)
Romano, Y., Elad, M., and Milanfar, P · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L · 2017
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Phase retrieval under reduced measurements for fourier ptychography using conditional adversarial networks
Boominathan, L. and Mitra, K · 2018
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