[1] C Zuo, Q Chen. Computational optical imaging: an overview. Infrared Laser Eng, 51, 20220110(2022).

[2] J J Xiang, G Y Jiang, M Yu et al. No-reference light field image quality assessment using four-dimensional sparse transform. IEEE Trans Multimedia, 25, 457-472(2023).

[3] T Q Lv, Y C Wu, X L Zhao. Light field image super-resolution network based on angular difference enhancement. Opto-Electron Eng, 50, 220185(2023).

[4] M Yu, C Liu. Single exposure light field imaging based all-in-focus image reconstruction technology. J Appl Opt, 42, 71-78(2021).

[5] Y Tian, H Q Zeng, L Xing et al. A multi-order derivative feature-based quality assessment model for light field image. J Vis Commun Image Represent, 57, 212-217(2018).

[6] H L Huang, H Q Zeng, J H Hou et al. A spatial and geometry feature-based quality assessment model for the light field images. IEEE Trans Image Process, 31, 3765-3779(2022).

[7] X K Min, J T Zhou, G T Zhai et al. A metric for light field reconstruction, compression, and display quality evaluation. IEEE Trans Image Process, 29, 3790-3804(2020).

[8] L K Shi, W Zhou, Z B Chen et al. No-reference light field image quality assessment based on spatial-angular measurement. IEEE Trans Circuits Syst Video Technol, 30, 4114-4128(2020).

[9] Z Y Luo, W Zhou, L K Shi et al. No-reference light field image quality assessment based on micro-lens image, 1-5(2019). https://doi.org/10.1109/PCS48520.2019.8954551

[10] J J Xiang, M Yu, H Chen et al. VBLFI: visualization-based blind light field image quality assessment, 1-6(2020). https://doi.org/10.1109/ICME46284.2020.9102963

[11] K Lamichhane, F Battisti, P Paudyal et al. Exploiting saliency in quality assessment for light field images, 1-5(2021). https://doi.org/10.1109/PCS50896.2021.9477451

[12] W Zhou, L K Shi, Z B Chen et al. Tensor oriented no-reference light field image quality assessment. IEEE Trans Image Process, 29, 4070-4084(2020).

[13] J J Xiang, M Yu, G Y Jiang et al. Pseudo video and refocused images-based blind light field image quality assessment. IEEE Trans Circuits Syst Video Technol, 31, 2575-2590(2021).

[14] S Alamgeer, M C Q Farias. No-Reference light field image quality assessment method based on a long-short term memory neural network, 1-6(2022). https://doi.org/10.1109/ICMEW56448.2022.9859419

[15] Q Qu, X M Chen, V Chung et al. Light field image quality assessment with auxiliary learning based on depthwise and anglewise separable convolutions. IEEE Trans Broadcast, 67, 837-850(2021).

[16] Q Qu, X M Chen, Y Y Chung et al. LFACon: Introducing anglewise attention to no-reference quality assessment in light field space. IEEE Trans Vis Comput Graph, 29, 2239-2248(2023).

[17] P Zhao, X M Chen, V Chung et al. DeLFIQE: a low-complexity deep learning-based light field image quality evaluator. IEEE Trans Instrum Meas, 70, 5014811(2021).

[18] Z Y Zhang, S S Tian, W B Zou et al. Deeblif: deep blind light field image quality assessment by extracting angular and spatial information, 2266-2270(2022). https://doi.org/10.1109/ICIP46576.2022.9897951

[19] K M He, X Y Zhang, S Q Ren et al. Deep residual learning for image recognition, 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[20] S Y Lu, Y M Ding, M Z Liu et al. Multiscale feature extraction and fusion of image and text in VQA. Int J Comput Intell Syst, 16, 54(2023).

[21] L K Shi, S Y Zhao, W Zhou et al. Perceptual evaluation of light field image, 41-45(2018). https://doi.org/10.1109/ICIP.2018.8451077

[22] Z J Huang, M Yu, G Y Jiang et al. Reconstruction distortion oriented light field image dataset for visual communication, 1-5(2019). https://doi.org/10.1109/ISNCC.2019.8909170

[23] L Shan, P An, C L Meng et al. A no-reference image quality assessment metric by multiple characteristics of light field images. IEEE Access, 7, 127217-127229(2019).

[24] A Mittal, A K Moorthy, A C Bovik. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process, 21, 4695-4708(2012).

[25] Q H Li, W S Lin, Y M Fang. No-reference quality assessment for multiply-distorted images in gradient domain. IEEE Signal Process Lett, 23, 541-545(2016).

[26] C L Meng, P An, X P Huang et al. Full reference light field image quality evaluation based on angular-spatial characteristic. IEEE Signal Process Lett, 27, 525-529(2020).

[27] L K Shi, S Y Zhao, Z B Chen. Belif: blind quality evaluator of light field image with tensor structure variation index, 3781-3785(2019). https://doi.org/10.1109/ICIP.2019.8803559

[28] Z Y Zhang, S S Tian, W B Zou et al. Blind quality assessment of light field image based on spatio-angular textural variation, 2385-2389(2023). https://doi.org/10.1109/ICIP49359.2023.10222216

[29] M Rerabek, T Ebrahimi. New light field image dataset, 1-2(2016).