[1] H. O.Anger. Use of a gamma-ray pinhole camera for in vivo studies. Nature, 170, 200-201(1952).

[2] B.Duan, D.Hei, G.Li, Y.Li, L.Sheng et al. Monte Carlo simulation of spatial resolution of lens-coupled LYSO scintillator for intense pulsed gamma-ray imaging system with large field of view. Nucl. Eng. Technol., 56, 2650-2658(2024).

[3] D.Chen, L.Li, Q.Yi, F.Zhang, J.Zhanget?al.. Simulation and experimental study of the angle-dependent sensitivity of the thick pinhole used for gamma imaging. Nucl. Instrum. Methods Phys. Res., Sect. A, 915, 24-30(2019).

[4] R.Ballabriga, A.Dragone, A. E.Gleason, A. F. T.Leong, Z.Wanget?al.. Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications. Nucl. Instrum. Methods Phys. Res., Sect. A, 1057, 168690(2023).

[5] A.Clausse, P.Knoblauch, F. D.Lorenzo, C.Moreno, V.Raspa. Hard X-ray dosimetry of a plasma focus suitable for industrial radiography. Radiat. Phys. Chem., 145, 39-42(2018).

[6] B. A.Apotovsky, F. L.Augustine, H. B.Barber, H. H.Barrett, E. L.Dereniaket?al.. Semiconductor pixel detectors for gamma-ray imaging in nuclear medicine. Nucl. Instrum. Methods Phys. Res., Sect. A, 395, 421-428(1997).

[7] S.Abdulwajid, A.-S.Al-Mohr, A. M.Alyassin, H. A.Maqsoud, A. M.Mashat. Feasibility study of gamma-ray medical radiography. Appl. Radiat. Isot., 72, 16-29(2013).

[8] C.Caliri, G. A. P.Cirrone, G.Cuttone, L.Giuffrida, G.Petringaet?al.. Study of gamma-ray emission by proton beam interaction with injected Boron atoms for future medical imaging applications. J. Instrum., 12, C03049(2017).

[9] N.Birge, D. N.Fittinghoff, V.Geppert-Kleinrath. Neutron imaging of inertial confinement fusion implosions. Rev. Sci. Instrum., 94, 021101(2023).

[10] B.Bi, L.Shan, C.Tian, F.Wu, M.Yuet?al.. Diagnosis of indirectly driven double shell targets with point-projection hard x-ray radiography. Matter Radiat. Extremes, 9, 027602(2024).

[11] N.Birge, A.DeYoung, D.Fittinghoff, V.Geppert-Kleinrath, N.Hoffmanet?al.. Gamma-ray imaging of inertial confinement fusion implosions reveals remaining ablator carbon distribution. Phys. Plasmas, 30, 022703(2023).

[12] Z.Gong, K. Z.Hatsagortsyan, C. H.Keitel. Deciphering in situ electron dynamics of ultrarelativistic plasma via polarization pattern of emitted γ-photons. Phys. Rev. Res., 4, L022024(2022).

[13] Z.Gong, K. Z.Hatsagortsyan, C. H.Keitel, X.Shen. Electron slingshot acceleration in relativistic preturbulent shocks explored via emitted photon polarization. Phys. Rev. Lett., 131, 225101(2023).

[14] T.Da, B.Duan, W.Gu, C.Han, J.Maet?al.. Pulsed radiation image restoration based on unsupervised deep learning. Nucl. Instrum. Methods Phys. Res., Sect. A, 1061, 169128(2024).

[15] C. A.Barrera, M. J.Moran, E. C.Morse. Image reconstruction algorithms for inertial confinement fusion neutron imaging. Rev. Sci. Instrum., 77, 10E716(2006).

[16] R. J.Ellis, S. M.Lane, R. A.Lerche, K. A.Nugent, D.Ress. Neutron penumbral imaging of laser-fusion targets. Laser Part. Beams, 9, 99-118(1991).

[17] W. H.Richardson. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., 62, 55-59(1972).

[18] R.Lemieux, G. E.Mailloux, R.Noumeir. An expectation maximization reconstruction algorithm for emission tomography with non-uniform entropy prior. Int. J. Biomed. Comput., 39, 299-310(1995).

[19] Y.Park, L.Reichel, G.Rodriguez, X.Yu. Parameter determination for Tikhonov regularization problems in general form. J. Comput. Appl. Math., 343, 12-25(2018).

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

[21] H.Hu, Q.Jia, D.Wang, F.Zhang. Source reconstruction for neutron coded-aperture imaging: A sparse method. Rev. Sci. Instrum., 88, 083502(2017).

[22] J.He, Q.Li, F.Wang, Y.Yan. Combination algorithms applied to source reconstruction for neutron coded images and restoration for incomplete coded images. Rev. Sci. Instrum., 94, 053301(2023).

[23] C.He, G.Hu, H.Hu, Z.Liu, M.Yan et al. Comparison of heuristic and deterministic algorithms in neutron coded imaging reconstruction. Nucl. Instrum. Methods Phys. Res., Sect. A, 985, 164704(2021).

[24] P. S.Cheol, K. M.Gi, P. M.Kyu. Super-resolution image reconstruction: A technical overview. IEEE Signal Process. Mag., 20, 21-36(2003).

[25] K.-K.Ma, J.Tian. A survey on super-resolution imaging. Signal, Image Video Process., 5, 329-342(2011).

[26] Y.Tian, W.Wang, J. H.Xue, W.Yang, X.Zhang et al. Deep learning for single image super-resolution: A brief review. IEEE Trans. Multimedia, 21, 3106-3121(2019).

[27] J.Chen, S. C. H.Hoi, Z.Wang. Deep learning for image super-resolution: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 43, 3365-3387(2021).

[28] W.Dong, X.Li, G.Shi, L.Zhang. Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process., 22, 1620-1630(2013).

[29] W.Gao, J.Zhang, D.Zhao. Group-based sparse representation for image restoration. IEEE Trans. Image Process., 23, 3336-3351(2014).

[30] X.He, T. Q.Nguyen, C.Ren, Q.Teng, Y.Wu. Single image super-resolution using local geometric duality and non-local similarity. IEEE Trans. Image Process., 25, 2168-2183(2016).

[31] C.Dong, K.He, C. C.Loy, X.Tang. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell., 38, 295-307(2016).

[32] H.Kim, K. M.Lee, B.Lim, S.Nah, S.Son. Enhanced deep residual networks for single image super-resolution, 1132-1140(2017).

[33] J.Caballero, A.Cunningham, F.Huszár, C.Ledig, L.Theiset?al.. Photo-realistic single image super-resolution using a generative adversarial network, 105-114(2017).

[34] S.Gao, X.Zhuang. Bayesian image super-resolution with deep modeling of image statistics. IEEE Trans. Pattern Anal. Mach. Intell., 45, 1405-1423(2023).

[35] P.Gong, X.Tang, P.Wang, R.Zhang, C.Zhouet?al.. Reconstruction method for gamma-ray coded-aperture imaging based on convolutional neural network. Nucl. Instrum. Methods Phys. Res., Sect. A, 934, 41-51(2019).

[36] Z.Li, R.Shi, X.-G.Tuo, C.-M.Wang, G.Yanget?al.. Reconstruction of tomographic gamma scanning transmission image from sparse projections based on convolutional neural networks. Nucl. Instrum. Methods Phys. Res., Sect. A, 1039, 167110(2022).

[37] J. J.Ahn, H.-J.Choi, J.Jeong, S. G.Kim, H.Leeet?al.. Image reconstruction method of gamma emission tomography based on prior-aware information and machine learning for partial-defect detection of PWR-type spent nuclear fuel. Nucl. Eng. Technol., 56, 4770-4781(2024).

[38] J.Chen, Z.Chen, J.Song, F.Wang, J.Zheng. Neutron penumbral image reconstruction with a convolution neural network using fast Fourier transform. Rev. Sci. Instrum., 95, 013509(2024).

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

[40] S.Boyd, E.Chu, J.Eckstein, N.Parikh, B.Peleato. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn., 3, 1-122(2011).

[41] Y.Chengda, D.Geman. Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Process., 4, 932-946(1995).

[42] M.Elad, P.Milanfar, Y.Romano. The little engine that could: Regularization by denoising (RED). SIAM J. Imaging Sci., 10, 1804-1844(2017).

[43] Y.Chen, D.Meng, K.Zhang, L.Zhang, W.Zuo. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Process., 26, 3142-3155(2017).

[44] K.Zhang, L.Zhang, W.Zuo. FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process., 27, 4608-4622(2018).

[45] Y.Li, L.Van Gool, K.Zhang, L.Zhang, W.Zuo et al. Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell., 44, 6360-6376(2022).

[46] W.An, T.Liu, Y.Wang, J.Yang, Q.Yin. Combining deep denoiser and low-rank priors for infrared small target detection. Pattern Recognit., 135, 109184(2023).

[47] Y.Fu, Z.Lai, K.Wei. Deep plug-and-play prior for hyperspectral image restoration. Neurocomputing, 481, 281-293(2022).

[48] Y.Chen, X.Gui, W.He, J.Zeng, X. L.Zhao. Combining low-rank and deep plug-and-play priors for snapshot compressive imaging. IEEE Trans. Neural Netw. Learn. Sys., 35, 16396-16408(2023).

[49] D.Ding, B.Wan, J.Xie, F.Yang, S.Zhaoet?al.. Effects of yttrium content on intrinsic radioactivity energy spectra of LYSO:Ce crystals. Nucl. Instrum. Methods Phys. Res., Sect. A, 1001, 165263(2021).

[50] S. i.Amari. Backpropagation and stochastic gradient descent method. Neurocomputing, 5, 185-196(1993).

[51] B.Duan, D.Hei, G.Li, Y.Li, L.Sheng, Q.Xing. Super-resolution image reconstruction of a neutron thick pinhole imaging system using image denoiser prior based on half quadratic splitting method. Nucl. Instrum. Methods Phys. Res., Sect. A, 1061, 169130(2024).

[52] A.Buades, B.Coll, J. M.Morel, 2, 60-65(2005).

[53] K.Dabov, K.Egiazarian, A.Foi, V.Katkovnik. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., 16, 2080-2095(2007).

[54] T.Brox, P.Fischer, O.Ronneberger. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), 9351, 234-241(2015).

[55] S. R. K.He, J.Sun, X.Zhang. Deep residual learning for image recognition, 770-778(2016).

[56] Y.Chen, T.Pock. Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell., 39, 1256-1272(2017).

[57] E.Agustsson, R.Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study, 126-135(2017).

[58] H.Kim, K. M.Lee, B.Lim, S.Nah, S.Son. Enhanced deep residual networks for single image super-resolution, 136-144(2017).

[59] J.Ba, D. P.Kingma. Adam: A method for stochastic optimization(2017).

[60] S. H.Chan, O. A.Elgendy, X.Wang. Plug-and-play ADMM for image restoration: Fixed-point convergence and applications. IEEE Trans. Comput. Imaging, 3, 84-98(2017).

[61] M. S. C.Almeida, M.Figueiredo. Deconvolving images with unknown boundaries using the alternating direction method of multipliers. IEEE Trans. Image Process., 22, 3074-3086(2013).

[62] J. A.Fessler, A.Matakos, S.Ramani. Accelerated edge-preserving image restoration without boundary artifacts. IEEE Trans. Image Process., 22, 2019-2029(2013).

[63] C. C.Paige, M. A.Saunders. LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Software, 8, 43-71(1982).

[64] C. R.Danly, D. N.Fittinghoff, G. P.Grim, N.Guler, P.Volegovet?al.. Neutron source reconstruction from pinhole imaging at national ignition facility. Rev. Sci. Instrum., 85, 023508(2014).