[1] [1] Wang Y, Huang K, Fang J,et al. Mid-infrared single-pixel imaging at the single-photon level［J］.Nat Commun,2023,14:1073.

[2] [2] Guo Y, Li B, Yin X. Dual-compressed photoacoustic single-pixel imaging［J］. National Science Review,2023,10:058.

[3] [3] Ferri F, Magatti D, Lugiato L A, et al. Differential ghost imaging［J］. Physical Review Letters,2010,104(25):253603.

[4] [4] Lyu M, Wang W, Wang H, et al. Deep-learning-based ghost imaging［J］. Scientific Reports,2017,7:17865.

[5] [5] Sun M J, Meng L T, Edgar M P. A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging［J］. Scientific Reports,2017,7(1):3464.

[6] [6] Zhang Z, Ma X, Zhong J. Single-pixel imaging by means of Fourier spectrum acquisition［J］. Nature Communications,2015,6(1):1—6.

[7] [7] Liu B, Yang Z, Liu X, et al. Coloured computational imaging with single-pixel detectors based on a 2D discrete cosine transform［J］. Journal of Modern Optics,2017,64(3):259—264.

[8] [8] C Ledig et al. Photo-realistic single image super-resolution using a generative adversarial network［C］∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA:IEEE,2017:105—114.

[9] [9] Shi, W, Caballero, J, Huszr, F. et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network［C］∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016:1874—1883.

[10] [10] Zonghao Liu, Huan Zhang, Mi Zhou, et al. Adaptive super-resolution networks for single-pixel imaging at ultra-low sampling rates［J］. IEEE Access,2024,12:78496—78504.

[12] [12] Zonghao Liu, Huan Zhang, Mi Zhou, et al. GAN-SRSPI: super-resolution single-pixel imaging using generative adversarial networks［C］∥Proc. SPIE 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications,2023:126177.

[13] [13] Shaozhen Sun, Wenjing Zhao, Aiping Zhai, et al. DCT single-pixel detecting for wavefront measurement［J］. Optics & Laser Technology,2023,163:109326.

[15] [15] Saad Michele A, Alan Conrad Bovik, Christophe Charrier. Blind image quality assessment: a natural scene statistics approach in the DCT domain［J］. IEEE Transactions on Image Processing,2012,21:3339—3352.