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Al-Bandawi, H and Deng, G (2019). Classification of image distortion based on the generalized Benford’s law. Multimedia Tools and Applications, pp. 1-18.

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Hao, X, Li, X, Wu, J, Wei, B, Song, Y and Li, B (2024). A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law. Remote Sensing, 16(7):1167. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Maza-Quiroga, R, Thurnhofer-Hemsi, K, Lopez-Rodrıguez, D and Lopez-Rubio, E (2021). Rician Noise Estimation for 3D Magnetic Resonance Images Based on Benford’s Law. In: de Bruijne M. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12906. Springer, Cham.. DOI:10.1007/978-3-030-87231-1_33. View Complete Reference Online information Works that this work references Works that reference this work
Maza-Quiroga, R, Thurnhofer-Hemsi, K, López-Rodríguez, D and López-Rubio, E (2023). Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit . Axioms 12, pp. 1117 . DOI:10.3390/axioms12121117. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Varga, D (2020). No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features. Journal of Imaging 6(8), 75. DOI:10.3390/jimaging6080075. View Complete Reference Online information Works that this work references Works that reference this work