Abstract
Low levels of lighting in images and videos may lead to poor results in segmentation, detection, tracking, among numerous other computer vision tasks. Deep-sea camera systems, such as those deployed on the Ocean Networks Canada (ONC) cabled ocean observatories, use artificial lighting to illuminate and capture videos of deep-water biological environments. When these lighting systems fail, the resulting images become hard to interpret or even completely useless because of their lighting levels. This paper proposes an effective framework to enhance the lighting levels of underwater images, increasing the number of visible, meaningful features. The process involves the dehazing of images using a dark channel prior and fast guided filters.
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Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, Barcelona, pp. 1–6 (2011)
Schechnner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Optics 42(3), 511–525 (2003)
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Tarel, J.P., Hautière, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, Kyoto (2009)
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision, Sydney, NSW, pp. 617–624 (2013)
Fattal, R.: Dehazing using color lines. ACM Trans. Graph. 34, 1–14 (2014)
Ancuti, C., Ancuti, C.O., Vleeschouwer, C.: D-Hazy: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE International Conf. on Image Processing (2016)
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision – ECCV 2016, pp 154–169, September 2016
Alharbi, E.B., Ge, P., Wang, H.: A research on single image dehazing algorithms based on dark channel prior. J. Comput. Commun. 4, 47–55 (2016)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846 (1988)
He, K., Sun, J.: Fast Guided Filter. eprint arXiv:1505.00996. Bibliographic code: 2015arXiv150500996H, May 2015
Lee, S., Yun, S., Nam, J., Won, C.S., Jung, S.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 2016, 4 (2016)
Tarel, J.-P., et al.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)
Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11752-2_3
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)
Anaya, J., Barbu, A.: RENOIR – a dataset for real low-light image noise reduction. J. Vis. Commun. Image Represent. 51(2), 144–154 (2018)
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Marques, T.P., Albu, A.B., Hoeberechts, M. (2019). Enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_6
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