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access icon free Modified grey world method to detect and restore colour cast images

This study proposes a new, simple but effective technique to detect and restore colour cast images, named modified grey world method. This method detects colour cast images of outdoor surveillance videos by computing the values in the YUV colour space, which makes it much easier than classic methods. Specific colour cast can be found out by calculating the hue values. Additionally, this method can detect not only simple colour cast images but also multiple colour cast images simultaneously. To detect and restore a colour cast image, the authors first remove all grey pixels and separate it into multiple parts with a maze-solving algorithm. Then, they compute the YUV colour values of each part. If the values are too high or too low, this part of the input image is designated as a colour cast. Finally, they carry out a restoration procedure, in which they calculate weights by matching average colour value with a grey reference value in YUV colour space. This method has been tested in the Safety City surveillance system in Wuhan city, China. The results show that the proposed method leads to better results in detecting and restoring colour cast imaging than classic methods in outdoor surveillance videos.

References

    1. 1)
      • 3. Celik, T., Yetgin, Z.: ‘Grey-wavelet: unifying grey-world and grey-edge colour constancy algorithms’, Signal Image Video Process., 2015, 9, (8), pp. 18891896.
    2. 2)
      • 1. Gijsenij, A., Gevers, T., Weijer, J.V.D.: ‘Computational color constancy: survey and experiments’, IEEE Trans. Image Process., 2011, 20, (9), pp. 24752489.
    3. 3)
      • 14. He, K., Sun, J., Tang, X.: ‘Single image haze removal using dark channel prior’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 33, (12), pp. 23412353.
    4. 4)
      • 15. Wyard-Scott, L., Meng, Q.H.M.: ‘A potential maze solving algorithm for a micromouse robot’. Proc. IEEE Pacific Rim Conf. IEEE Communications, Computers, and Signal Processing, Victoria, Canada, 1995, pp. 614618.
    5. 5)
      • 18. Liu, W., Chen, X., Chu, X., et al: ‘Haze removal for a single inland waterway image using sky segmentation and dark channel prior’, IET Image Process., 2017, 10, (12), pp. 9961006.
    6. 6)
      • 10. Barnard, K., Cardei, V., Funt, B.: ‘Estimating the scene illumination chromaticity using a neural network’, J. Opt. Soc. Am. A, 2002, 19, (12), pp. 23742386.
    7. 7)
      • 5. Banic, N., Loncaric, S.: ‘Improving the white patch method by subsampling’, 2014, doi: 10.1109/ICIP.2014.7025121.
    8. 8)
      • 9. Zou, X., Shen, Z., Kang, J., et al: ‘An improved colour cast feature and feature extraction method based on lab chromaticity histogram’, 2016, doi: 10.1109/SIPROCESS.2016.7888237.
    9. 9)
      • 4. Yoo, J., Kyung, W., Choi, J., et al: ‘Colour image enhancement using weighted multi-scale compensation based on the grey world assumption’, J. Imaging Sci. Technol., 2017, 61, (3), pp. 113.
    10. 10)
      • 6. Thai, B., Deng, G., Ross, R.: ‘A fast white balance algorithm based on pixel greyness’, Signal Image Video Process., 2017, 11, (3), pp. 525532.
    11. 11)
      • 11. Ju, J., Park, R.H.: ‘Colour fringe detection and correction in colour space’, IET Image Process., 2013, 7, (4), pp. 300309.
    12. 12)
      • 16. Marukatat, S.: ‘Image enhancement using local intensity distribution equalization’, EURASIP J. Image Video Process., 2015, 1, pp. 118.
    13. 13)
      • 2. Tastl, I.: ‘Novel approach to colour cast detection and removal in digital images’, Proc. SPIE – Int. Soc. Opt. Eng., 2000, 3963, pp. 167175.
    14. 14)
      • 13. Tan, X., Lai, S., Wang, B., et al: ‘A simple grey-edge automatic white balance method with FPGA implementation’, J. Real-Time Image Process., 2015, 10, (2), pp. 207217.
    15. 15)
      • 7. Yan, Z., Zhang, H., Wang, B., et al: ‘Automatic photo adjustment using deep neural networks’, ACM Trans. Graph. (TOG), 2016, 35, (2), pp. 115.
    16. 16)
      • 12. Tanaka, S., Kakinuma, A., Kamijo, N., et al: ‘Auto white balance method using a pigmentation separation technique for human skin color’, Opt. Rev., 2016, 24, (1), pp. 110.
    17. 17)
      • 17. Xie, Z.X., Wang, Z.F.: ‘Color image quality assessment based on image quality parameters perceived by human vision system’. Int. Conf. Multimedia Technology (ICMT), Ningbo, 29–31 October 2010.
    18. 18)
      • 8. Lin, H., Lin, C.: ‘Using a hybrid of fuzzy theory and neural network filter for single image dehazing’, Appl. Intell., 2017, 47, (4), pp. 10991114.
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