1. [1] Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I.:" A compilation of UAV applications for precision agricultura" Computer Networks."172, 107148 (2020). [DOI:10.1016/j.comnet.2020.107148]

2. [2] Senthilnath, J., Kandukuri, M., Dokania, A., Ramesh, K.N.: "Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods". Comput Electron Agric. 140, 8-24 (2017). [DOI:10.1016/j.compag.2017.05.027]

3. [3] Koutalakis, P., Tzoraki, O., Zaimes, G.: "Uavs for hydrologic scopes: Application of a low-cost UAV to estimate surface water velocity by using three different image-based methods" Drones."3, 1-15 (2019). [DOI:10.3390/drones3010014]

4. [4] Santise, M., Thoeni, K., Roncella, R., Diotri, F., Giacomini, A.: "Analysis of low-light and night-time stereo-pair images for photogrammetric reconstruction. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences" - ISPRS Archives. 42, 1015-1022 (2018). [DOI:10.5194/isprs-archives-XLII-2-1015-2018]

5. [5] Dash, J.P., Watt, M.S., Pearse, G.D., Heaphy, M., Dungey, H.S.: "Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak." ISPRS Journal of Photogrammetry and Remote Sensing. 131, 1-14 (2017). [DOI:10.1016/j.isprsjprs.2017.07.007]

6. [6] Cao, S., Danielson, B., Clare, S., Koenig, S., Campos-Vargas, C., Sanchez-Azofeifa, A.: "Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols." ISPRS Journal of Photogrammetry and Remote Sensing. 149, 132-145 (2019). [DOI:10.1016/j.isprsjprs.2019.01.016]

7. [7] Burdziakowski, P., Bobkowska, K.: "Uav photogrammetry under poor lighting conditions-accuracy considerations." Sensors. 21, (2021). [DOI:10.3390/s21103531]

8. [8] Korneliussen, J.T., Hirakawa, K.: " Uav photogrammetry under poor lighting conditions-accuracy considerations." IEEE Transactions on Image Processing. 23, 4539-4552 (2014). [DOI:10.1109/TIP.2014.2350911]

9. [9] Kedzierski, M., Wierzbicki, D., Sekrecka, A., Fryskowska, A., Walczykowski, P., Siewert, J.: "Influence of lower atmosphere on the radiometric quality of unmanned aerial vehicle imagery." Remote Sens (Basel). 11, (2019). [DOI:10.3390/rs11101214]

10. [10] Nafchi, H.Z., Shahkolaei, A., Hedjam, R., Cheriet, M.:" Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator." IEEE Access. 4, 5579-5590 (2016). [DOI:10.1109/ACCESS.2016.2604042]

11. [11] Sun, W., Liao, Q., Xue, J.H., Zhou, F.: SPSIM:" A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment." IEEE Transactions on Image Processing. 27, 4232-4244 (2018). [DOI:10.1109/TIP.2018.2837341]

12. [12] Bae, T.W.: "Image-quality metric system for color filter array evaluation." PLoS One. 15, (2020). [DOI:10.1371/journal.pone.0232583]

13. [13] Frackiewicz, M., Szolc, G., Palus, H.: "An improved SPSIM index for image quality assessment." Symmetry (Basel). 13, (2021). [DOI:10.20944/preprints202102.0189.v1]

14. [14] "no-reference-image-quality-assessment-using-blur-and-noise".

15. [15] Zhou, L.Y., Zhang, Z.B.: "No-reference image quality assessment based on noise, blurring and blocking effect." Optik (Stuttg). 125, 5677-5680 (2014). [DOI:10.1016/j.ijleo.2014.07.010]

16. [16] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: "Image quality assessment: From error visibility to structural similarity." IEEE Transactions on Image Processing. 13, 600-612 (2004). [DOI:10.1109/TIP.2003.819861]

17. [17] Wang, T., Zhang, L., Jia, H., Li, B., Shu, H., Multiscale, S.: "Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment." Signal Process Image Commun. 45, 1-9 (2016). [DOI:10.1016/j.image.2016.04.005]

18. [18] Neumann, L., Sbert, M., Gooch, B.: "Global Contrast Factor-a New Approach to Image Contrast." W. Purgathofer (2005)

19. [19] Sieberth, T., Wackrow, R., Chandler, J.H.:"Automatic detection of blurred images in UAV image sets " ISPRS Journal of Photogrammetry and Remote Sensing. 122, 1-16 (2016). [20] Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., Al-Quaderi, G.D., Shoyaib, M.: "An adaptive gamma correction for image enhancement." EURASIP J Image Video Process. 2016, (2016). https://doi.org/10.1186/s13640-016-0138-1 [DOI:10.1016/j.isprsjprs.2016.09.010]

20. [21] Singh, K., Vishwakarma, D.K., Walia, G.S., Kapoor, R.:" Contrast enhancement via texture region based histogram equalization." J Mod Opt. 63, 1444-1450 (2016). [DOI:10.1080/09500340.2016.1154194]

21. [22] Łabędź, P., Skabek, K., Ozimek, P., Nytko, M.: "Histogram adjustment of images for improving photogrammetric reconstruction. Sensors." 21, (2021). [DOI:10.3390/s21144654]

22. [23] Wong, C.Y., Liu, S., Liu, S.C., Rahman, M.A., Lin, S.C.F., Jiang, G., Kwok, N., Shi, H.:" Image contrast enhancement using histogram equalization with maximum intensity coverage. "J Mod Opt. 63, 1618-1629 (2016). [24] Mayathevar, K., Veluchamy, M., Subramani, B.:" Fuzzy color histogram equalization with weighted distribution for image enhancement." Optik (Stuttg). 216, 164927 (2020). https://doi.org/10.1016/j.ijleo.2020.164927 [25] Mohsin Abdulazeez, A., Zeebaree, D.Q., Zebari, D.A., Zebari, G.M., Mohammed, I., Adeen, N.: Journal of Soft Computing and Data Mining The Applications of Discrete Wavelet Transform in Image Processing: A Review. JOURNAL OF SOFT COMPUTING AND DATA MINING. 1, 31-43 (2020). [DOI:10.1080/09500340.2016.1163428]

23. [26] Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., Ma, J.: "MSR-net:Low-light Image Enhancement Using Deep Convolutional Network." (2017)

24. [27] Zhao, J., Chen, H., Zeng, S., Ma, C.: "RISSNet: Retain low-light image details and improve the structural similarity net. IET Image Process." 16, 1793-1806 (2022). [DOI:10.1049/ipr2.12446]

25. [28] Lu, C.T., Wang, L.L., Shen, J.H., Lin, J.A.:" Image enhancement using deep-learning fully connected neural network mean filter." Journal of Supercomputing. 77, 3144-3164 (2021). [29] Hai, J., Xuan, Z., Han, S., Yang, R., Hao, Y., Zou, F., Lin, F.:" R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network." (2021). [DOI:10.1007/s11227-020-03389-6]

26. [30] Li, X., Hu, H., Zhao, L., Wang, H., Yu, Y., Wu, L., Liu, T.: "Polarimetric image recovery method combining histogram stretching for underwater imaging." Sci Rep.

27. [31] Hu, L., Qin, M., Zhang, F., Du, Z., Liu, R.:" RSCNN: A cnn-based method to enhance low-light remote-sensing images," (2021). [DOI:10.3390/rs13010062]

28. [32] Wei, C., Wang, W., Yang, W., Liu, J.:" Deep Retinex Decomposition for Low-Light Enhancement." (2018).