• Laser & Optoelectronics Progress
  • Vol. 60, Issue 2, 0200001 (2023)
Liheng Bian1、2、**, Daoyu Li1、2, Xuyang Chang1、2, and Jinli Suo3、*
Author Affiliations
  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
  • 2Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China
  • 3Department of Automation, Tsinghua University, Beijing 100084, China
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    DOI: 10.3788/LOP221245 Cite this Article Set citation alerts
    Liheng Bian, Daoyu Li, Xuyang Chang, Jinli Suo. Theory and Approach of Large-Scale Computational Reconstruction[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0200001 Copy Citation Text show less
    References

    [1] Brady D J, Gehm M E, Stack R A et al. Multiscale gigapixel photography[J]. Nature, 486, 386-389(2012).

    [2] Liu F, Wu X Q, Zhao L et al. Research progress of wide-field and high-resolution computational optical imaging system[J]. Laser & Optoelectronics Progress, 58, 1811001(2021).

    [3] Zheng G A, Horstmeyer R, Yang C. Wide-field, high-resolution Fourier ptychographic microscopy[J]. Nature Photonics, 7, 739-745(2013).

    [4] Fan J T, Suo J L, Wu J M et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution[J]. Nature Photonics, 13, 809-816(2019).

    [5] Park J, Brady D J, Zheng G et al. Review of bio-optical imaging systems with a high space-bandwidth product[J]. Advanced Photonics, 3, 044001(2021).

    [6] Chang X, Bian L, Zhang J. Large-scale phase retrieval[J]. eLight, 34-45(2021).

    [7] Gerchberg R W. A practical algorithm for the determination of phase from image and diffraction plane pictures[J]. Optik, 35, 237-246(1972).

    [8] Fienup J R. Phase retrieval algorithms: a comparison[J]. Applied Optics, 21, 2758-2769(1982).

    [9] Sun M J, Yan S M, Wang S Y. Reconstruction algorithms for ghost imaging and single-pixel imaging[J]. Laser & Optoelectronics Progress, 59, 0200001(2022).

    [10] Guo K K, Jiang S W, Zheng G A. Multilayer fluorescence imaging on a single-pixel detector[J]. Biomedical Optics Express, 7, 2425-2431(2016).

    [11] Eldar Y C, Mendelson S. Phase retrieval: stability and recovery guarantees[J]. Applied and Computational Harmonic Analysis, 36, 473-494(2014).

    [12] Shechtman Y, Eldar Y C, Cohen O et al. Phase retrieval with application to optical imaging: a contemporary overview[J]. IEEE Signal Processing Magazine, 32, 87-109(2015).

    [13] Miao J W, Charalambous P, Kirz J et al. Extending the methodology of X-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens[J]. Nature, 400, 342-344(1999).

    [14] Bauschke H H, Combettes P L, Luke D R. Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization[J]. Journal of the Optical Society of America, 19, 1334-1345(2002).

    [15] Elser V. Solution of the crystallographic phase problem by iterated projections[J]. Acta Crystallographica Section A: Foundations of Crystallography, 59, 201-209(2003).

    [16] Bauschke H H, Combettes P L, Luke D R. Hybrid projection-reflection method for phase retrieval[J]. Journal of the Optical Society of America, 20, 1025-1034(2003).

    [17] Chen C C, Miao J W, Wang C W et al. Application of optimization technique to noncrystalline X-ray diffraction microscopy: guided hybrid input-output method[J]. Physical Review B, 76, 064113(2007).

    [18] Luke D R. Relaxed averaged alternating reflections for diffraction imaging[J]. Inverse Problems, 21, 37(2004).

    [19] Martin A V, Wang F, Loh N D et al. Noise-robust coherent diffractive imaging with a single diffraction pattern[J]. Optics Express, 20, 16650-16661(2012).

    [20] Rodriguez J A, Xu R, Chien C C et al. Oversampling smoothness: an effective algorithm for phase retrieval of noisy diffraction intensities[J]. Journal of Applied Crystallography, 46, 312-318(2013).

    [21] Mao H F, Zhao J F, Cui G M et al. LED array position correction method based on Fourier ptychographic microscopy[J]. Acta Optica Sinica, 41, 0411002(2021).

    [22] Dong S Y, Horstmeyer R, Shiradkar R et al. Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging[J]. Optics Express, 22, 13586-13599(2014).

    [23] Bian L H, Suo J L, Zheng G A et al. Fourier ptychographic reconstruction using Wirtinger flow optimization[J]. Optics Express, 23, 4856-4866(2015).

    [24] Bian L H, Suo J L, Chung J et al. Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient[J]. Scientific Reports, 6, 27384(2016).

    [25] Zheng G A, Shen C, Jiang S W et al. Concept, implementations and applications of Fourier ptychography[J]. Nature Reviews Physics, 3, 207-223(2021).

    [26] Williams G J, Quiney H M, Dhal B B et al. Fresnel coherent diffractive imaging[J]. Physical Review Letters, 97, 025506(2006).

    [27] Pfeifer M A, Williams G J, Vartanyants I A et al. Three-dimensional mapping of a deformation field inside a nanocrystal[J]. Nature, 442, 63-66(2006).

    [28] Rodenburg J M, Hurst A C, Cullis A G et al. Hard-X-ray lensless imaging of extended objects[J]. Physical Review Letters, 98, 034801(2007).

    [29] Li M, Bian L H, Zhang J. Coded coherent diffraction imaging with reduced binary modulations and low-dynamic-range detection[J]. Optics Letters, 45, 4373-4376(2020).

    [30] Miao J W, Ishikawa T, Robinson I K et al. Beyond crystallography: diffractive imaging using coherent X-ray light sources[J]. Science, 348, 530-535(2015).

    [31] Ulyanov D, Vedaldi A, Lempitsky V S. Deep image prior[C], 9446-9454(2018).

    [32] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[EB/OL]. https://arxiv.org/abs/1505.04597

    [33] Zou S F, Long M Z, Wang X Y et al. A CNN-based blind denoising method for endoscopic images[C](2019).

    [34] Park Y, Lee S, Jeong B et al. Joint demosaicing and denoising based on a variational deep image prior neural network[J]. Sensors, 20, 2970(2020).

    [35] Nie J T, Zhang L, Wang C et al. Robust deep hyperspectral imagery super-resolution[C], 847-850(2019).

    [36] Ma X F, Hong Y T, Song Y Z. Super resolution land cover mapping of hyperspectral images using the deep image prior-based approach[J]. International Journal of Remote Sensing, 41, 2818-2834(2020).

    [37] Zhang T, Fu Y, Wang L Z et al. Hyperspectral image reconstruction using deep external and internal learning[C], 8558-8567(2019).

    [38] Nie J T, Zhang L, Wei W et al. Unsupervised deep hyperspectral super-resolution with unregistered images[C](2020).

    [39] Voynov O, Artemov A, Egiazarian V et al. Perceptual deep depth super-resolution[C], 5652-5662(2019).

    [40] Sagel A, Roumy A, Guillemot C. Sub-dip: optimization on a subspace with deep image prior regularization and application to superresolution[C], 2513-2517(2020).

    [41] Li B Y, Gou Y B, Liu J Z et al. Zero-shot image dehazing[J]. IEEE Transactions on Image Processing, 29, 8457-8466(2020).

    [42] Feng Y Y, Shi Y, Sun D J. Blind poissonian image deblurring regularized by a denoiser constraint and deep image prior[J]. Mathematical Problems in Engineering, 2020, 9483521(2020).

    [43] Zukerman J, Tirer T, Giryes R. BP-DIP: a backprojection based deep image prior[C], 675-679(2021).

    [44] Weber T, Hußmann H, Han Z et al. Draw with me: human-in-the-loop for image restoration[C], 243-253(2020).

    [45] Zhang H T, Mai L, Jin H L et al. An internal learning approach to video inpainting[C], 2720-2729(2019).

    [46] Suzuki T. Superpixel segmentation via convolutional neural networks with regularized information maximization[C], 2573-2577(2020).

    [47] Jagatap G, Hegde C. Phase retrieval using untrained neural network priors[EB/OL]. https://openreview.net/pdf?id=r1l9n725IH

    [48] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

    [49] Liu Z W, Luo P, Wang X G et al. Deep learning face attributes in the wild[C], 3730-3738(2015).

    [50] Jiang S W, Guo K K, Liao J et al. Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow[J]. Biomedical Optics Express, 9, 3306-3319(2018).

    [51] Wang F, Bian Y M, Wang H C et al. Phase imaging with an untrained neural network[J]. Light: Science & Applications, 9, 77(2020).

    [52] Gong K, Kim K, Wu D F et al. Low-dose dual energy CT image reconstruction using non-local deep image prior[C](2019).

    [53] Darestani M Z, Heckel R. Accelerated MRI with un-trained neural networks[J]. IEEE Transactions on Computational Imaging, 7, 724-733(2021).

    [54] Ho K, Gilbert A, Jin H L et al. Neural architecture search for deep image prior[J]. Computers & Graphics, 98, 188-196(2021).

    [55] Sun Z D. Solving inverse problems with hybrid deep image priors: the challenge of preventing overfitting[EB/OL]. https://arxiv.org/abs/2011.01748

    [56] Liu J M, Sun Y, Xu X J et al. Image restoration using total variation regularized deep image prior[C], 7715-7719(2019).

    [57] Baguer D O, Leuschner J, Schmidt M. Computed tomography reconstruction using deep image prior and learned reconstruction methods[J]. Inverse Problems, 36, 094004(2020).

    [58] Fan W, Yu H, Chen T et al. OCT image restoration using non-local deep image prior[J]. Electronics, 9, 784(2020).

    [59] Zhao D, Zhao F, Gan Y J. Reference-driven compressed sensing MR image reconstruction using deep convolutional neural networks without pre-training[J]. Sensors, 20, 308(2020).

    [60] Qayyum A, Sultani W, Shamshad F et al. Single-shot retinal image enhancement using deep image priors[M]. Martel A L, Abolmaesumi P, Stoyanov D, et al. Medical image computing and computer assisted intervention-MICCAI 2020, 12265, 636-646(2020).

    [61] Yokota T, Kawai K, Sakata M et al. Dynamic PET image reconstruction using nonnegative matrix factorization incorporated with deep image prior[C], 3126-3135(2019).

    [62] Han S, Prince J L, Carass A. Inhomogeneity correction in magnetic resonance images using deep image priors[M]. Liu M X, Yan P K, Lian C F, et al. Machine learning in medical imaging, 12436, 404-413(2020).

    [63] Yang F S, Pham T A, Brandenberg N et al. Robust phase unwrapping via deep image prior for quantitative phase imaging[J]. IEEE Transactions on Image Processing, 30, 7025-7037(2021).

    [64] Qiao M, Liu X, Yuan X. Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks[J]. Optics Letters, 46, 1888-1891(2021).

    [65] Ljubenović M, Figueiredo M A T. Plug-and-play approach to class-adapted blind image deblurring[J]. International Journal on Document Analysis and Recognition (IJDAR), 22, 79-97(2019).

    [66] Chang X, Bian L, Gao Y et al. Plug-and-play pixel super-resolution phase retrieval for digital holography[J]. Optics Letters, 47, 2658-2661(2022).

    [67] Chan S H, Wang X R, Elgendy O A. Plug-and-play ADMM for image restoration: fixed-point convergence and applications[J]. IEEE Transactions on Computational Imaging, 3, 84-98(2017).

    [68] Zhang K, Zuo W M, Zhang L. Deep plug-and-play super-resolution for arbitrary blur kernels[C], 1671-1681(2019).

    [69] Lu H N, Tong C Y, Lian W et al. Deep plug-and-play video super-resolution[M]. Bartoli A, Fusiello A. Computer vision-ECCV 2020 workshops, 12538, 114-130(2020).

    [70] Kamilov U S, Mansour H, Wohlberg B. A plug-and-play priors approach for solving nonlinear imaging inverse problems[J]. IEEE Signal Processing Letters, 24, 1872-1876(2017).

    [71] Metzler C A, Schniter P, Veeraraghavan A et al. prDeep: robust phase retrieval with a flexible deep network[EB/OL]. https://arxiv.org/abs/1803.00212

    [72] Wu Z H, Sun Y, Liu J M et al. Online regularization by denoising with applications to phase retrieval[C], 3887-3895(2019).

    [73] Wei K X, Aviles-Rivero A, Liang J W et al. Tuning-free plug-and-play proximal algorithm for inverse imaging problems[C], 10158-10169(2020).

    [74] Sun Y, Xu S Q, Li Y Z et al. Regularized Fourier ptychography using an online plug-and-play algorithm[C], 7665-7669(2019).

    [75] Venkatakrishnan S V, Bouman C A, Wohlberg B. Plug-and-Play priors for model based reconstruction[C], 945-948(2013).

    [76] Sun Y, Wohlberg B, Kamilov U S. An online plug-and-play algorithm for regularized image reconstruction[J]. IEEE Transactions on Computational Imaging, 5, 395-408(2019).

    [77] Wu Z H, Sun Y, Matlock A et al. SIMBA: scalable inversion in optical tomography using deep denoising priors[J]. IEEE Journal of Selected Topics in Signal Processing, 14, 1163-1175(2020).

    [78] He R, Zheng W S, Tan T N et al. Half-quadratic-based iterative minimization for robust sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 261-275(2014).

    [79] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2, 183-202(2009).

    [80] Boyd S, Parikh N, Chu E[M]. Distributed optimization and statistical learning via the alternating direction method of multipliers(2011).

    [81] Yuan X. Generalized alternating projection based total variation minimization for compressive sensing[C], 2539-2543(2016).

    [82] Yuan X, Brady D J, Katsaggelos A K. Snapshot compressive imaging: theory, algorithms, and applications[J]. IEEE Signal Processing Magazine, 38, 65-88(2021).

    [83] Metzler C A, Maleki A, Baraniuk R G. BM3D-AMP: a new image recovery algorithm based on BM3D denoising[C], 3116-3120(2015).

    [84] Feng O Y, Venkataramanan R, Rush C et al. A unifying tutorial on approximate message passing[EB/OL]. https://arxiv.org/abs/2105.02180

    [85] Romano Y, Elad M, Milanfar P. The little engine that could: regularization by denoising (RED)[J]. SIAM Journal on Imaging Sciences, 10, 1804-1844(2017).

    [86] Ryu E K, Liu J L, Wang S C et al. Plug-and-play methods provably converge with properly trained denoisers[EB/OL]. https://arxiv.org/abs/1905.05406

    [87] Sun Y, Wu Z H, Xu X J et al. Scalable plug-and-play ADMM with convergence guarantees[J]. IEEE Transactions on Computational Imaging, 7, 849-863(2021).

    [88] Gavaskar R G, Chaudhury K N. Plug-and-play ISTA converges with kernel denoisers[J]. IEEE Signal Processing Letters, 27, 610-614(2020).

    [89] Liu T T, Xing L, Sun Z G. Study on convergence of plug-and-play ISTA with adaptive-kernel denoisers[J]. IEEE Signal Processing Letters, 28, 1918-1922(2021).

    [90] Yuan X, Liu Y, Suo J L et al. Plug-and-play algorithms for large-scale snapshot compressive imaging[C], 1444-1454(2020).

    [91] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2009).

    [92] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [93] Lipton Z C, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning[EB/OL]. https://arxiv.org/abs/1506.00019

    [94] Han K, Wang Y H, Chen H T et al. A survey on vision transformer[EB/OL]. https://arxiv.org/abs/2012.12556

    [95] Khan S, Naseer M, Hayat M et al. Transformers in vision: a survey[EB/OL]. https://arxiv.org/abs/2101.01169

    [96] Tolstikhin I, Houlsby N, Kolesnikov A et al. MLP-mixer: an all-MLP architecture for vision[EB/OL]. https://arxiv.org/abs/2105.01601

    [97] Liang J L, Liu R F. Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network[C], 697-701(2015).

    [98] Xu Q Y, Zhang C J, Zhang L. Denoising convolutional neural network[C], 1184-1187(2015).

    [99] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

    [100] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

    [101] Bae W, Yoo J, Ye J C. Beyond deep residual learning for image restoration: persistent homology-guided manifold simplification[C], 1141-1149(2017).

    [102] Wang T Y, Sun M X, Hu K N. Dilated deep residual network for image denoising[C], 1272-1279(2017).

    [103] Jifara W, Jiang F, Rho S et al. Medical image denoising using convolutional neural network: a residual learning approach[J]. The Journal of Supercomputing, 75, 704-718(2019).

    [104] Zhang K, Zuo W M, Zhang L. FFDNet: toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 27, 4608-4622(2018).

    [105] Guo S, Yan Z F, Zhang K et al. Toward convolutional blind denoising of real photographs[C], 1712-1722(2019).

    [106] Chen J W, Chen J W, Chao H Y et al. Image blind denoising with generative adversarial network based noise modeling[C], 3155-3164(2018).

    [107] Yuan Q Q, Zhang Q, Li J et al. Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1205-1218(2019).

    [108] Park J H, Kim J H, Cho S I. The analysis of CNN structure for image denoising[C], 220-221(2018).

    [109] Abbasi A, Monadjemi A, Fang L Y et al. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks[J]. Computers in Biology and Medicine, 108, 1-8(2019).

    [110] Su Y M, Lian Q S, Zhang X H et al. Multi-scale cross-path concatenation residual network for poisson denoising[J]. IET Image Processing, 13, 1295-1303(2019).

    [111] Liang J Y, Cao J Z, Sun G L et al. SwinIR: image restoration using swin transformer[C], 1833-1844(2021).

    [112] Zamir S W, Arora A, Khan S et al. Multi-stage progressive image restoration[C], 14821-14831(2021).

    [113] Shi B S, Lian Q S, Fan X Y. PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems[J]. Signal Processing, 162, 83-96(2019).

    [114] Mataev G, Milanfar P, Elad M. Deepred: deep image prior powered by red[EB/OL]. https://arxiv.org/abs/1903.10176

    [115] Wang Y T, Sun X H, Fleischer J W. When deep denoising meets iterative phase retrieval[C], 10007(2020).

    [116] Katkovnik V, Shevkunov I, Egiazarian K. ADMM and spectral proximity operators in hyperspectral broadband phase retrieval for quantitative phase imaging[EB/OL]. https://arxiv.org/abs/2105.07891

    [117] Chang X, Bian L, Jiang S et al. Plug-and-play optimization for pixel super-resolution phase retrieval[EB/OL]. https://arxiv.org/abs/2105.14746v1

    [118] Shastri S K, Ahmad R, Metzler C. Matching plug-and-play algorithms to the denoiser[EB/OL]. https://openreview.net/pdf?id=oRF0-zGYAei

    [119] Wang M, Deng W H. Deep visual domain adaptation: a survey[J]. Neurocomputing, 312, 135-153(2018).

    [120] Rivenson Y, Wu Y C, Ozcan A. Deep learning in holography and coherent imaging[J]. Light: Science & Applications, 8, 85(2019).

    [121] Goy A, Arthur K, Li S et al. Low photon count phase retrieval using deep learning[J]. Physical Review Letters, 121, 243902(2018).

    [122] Bian L H, Suo J L, Hu X M et al. Efficient single pixel imaging in Fourier space[J]. Journal of Optics, 18, 085704(2016).

    [123] Yu W K, Li M F, Yao X R et al. Adaptive compressive ghost imaging based on wavelet trees and sparse representation[J]. Optics Express, 22, 7133-7144(2014).

    [124] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

    [125] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16×16 words: Transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929v2

    [126] Gou J P, Yu B S, Maybank S J et al. Knowledge distillation: a survey[J]. International Journal of Computer Vision, 129, 1789-1819(2021).

    [127] Mansouri Y, Babar M A. A review of edge computing: features and resource virtualization[J]. Journal of Parallel and Distributed Computing, 150, 155-183(2021).

    [128] Coulouris G F, Dollimore J, Kindberg T et al[M]. Distributed systems: concepts and design(2011).

    [129] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 60, 259-268(1992).

    [130] Dong W S, Shi G M, Li X et al. Compressive sensing via nonlocal low-rank regularization[J]. IEEE Transactions on Image Processing, 23, 3618-3632(2014).

    [131] Wei K X, Fu Y, Yang J L et al. A physics-based noise formation model for extreme low-light raw denoising[C], 2758-2767(2020).

    [132] Elsken T, Metzen J H, Hutter F. Neural architecture search: a survey[EB/OL]. https://arxiv.org/abs/1808.05377

    [133] Sutton R S, Barto A G[M]. Reinforcement learning: an introduction(2018).

    Liheng Bian, Daoyu Li, Xuyang Chang, Jinli Suo. Theory and Approach of Large-Scale Computational Reconstruction[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0200001
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