A new multi-focus image fusion quality assessment method with convolutional sparse representation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03351-0.pdf
Reference59 articles.
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2. Liu, Y., Wang, L., Cheng, J., et al.: Multi-focus image fusion: a survey of the state of the art. Inf. Fusion 64, 71–91 (2020). https://doi.org/10.1016/j.inffus.2020.06.013
3. Liu, Y., Chen, X., Wang, Z., et al.: Deep learning for pixel-level image fusion: recent advances and future prospects. Inf. Fusion 42, 158–173 (2028). https://doi.org/10.1016/j.inffus.2017.10.007
4. Zhang, X.: Deep learning-based multi-focus image fusion: a survey and a comparative study. IEEE TPAMI 44(9), 4819–4838 (2022). https://doi.org/10.1109/TPAMI.2021.3078906
5. Zhai, G., Min, X.: Perceptual image quality assessment: a survey. Sci. China Inf. Sci. 63(11), 211301 (2022). https://doi.org/10.1007/s11432-019-2757-1
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