Abstract
Images captured in bad weather conditions such as fog, mist, haze, etc., are severely degraded due to the scattering of the particles in the atmosphere. These images are inappropriate for various applications of computer vision, e.g., transportation, remote sensing, video surveillance object recognition, etc. Image dehazing is the process of removing the haze effect from an image so that these applications can be benefited. The physical model of haze formation is used to restore a hazy image which requires two parameters to estimate: transmission and airlight. The accuracy of the dehazing depends on the estimation of the transmission. Dark channel prior (DCP) is an effective method to compute the transmission. However, a dark channel underestimates the transmission when an object in the scene has a similar color to the atmospheric light or sky region, as a result, the dehazed image looks dark. In this paper, we explore the DCP from a new perspective and reformulate it into contrast, saturation and brightness. We proposed a method to estimate the transmission without computing the dark channel. To overcome the problem of over-enhancement and remove the haze effect, a nonlinear model based on inverse strategy is introduced. It prevents the transmission from becoming over-estimated or under-estimated. The experimental result section demonstrates the efficacy of the proposed method over the natural and synthetic hazy images along with qualitative and quantitative analysis.
Similar content being viewed by others
References
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition, pp. 779–788 (2016)
Gupta, N., Jalal, A.S.: Traditional to transfer learning progression on scene text detection and recognition: a survey. Artif. Intell. Rev. 55, 3457–3502 (2022). https://doi.org/10.1007/s10462-021-10091-3
Li, D., Zhang, Z., Yu, K., Huang, K., Tan, T.: ISEE: an intelligent scene exploration and evaluation platform for large-scale visual surveillance. IEEE Trans. Parallel Distrib. Syst. 30(12), 2743–2758 (2019). https://doi.org/10.1109/TPDS.2019.2921956
Woodell, G., Jobson, D.J., Rahman, Z., Hines, G.: Advanced image processing of aerial imagery. Proc. SPIE 6246, 1–12 (2006)
Garg, H., Agrawal, A.: A comparative study on vehicles safety systems. In: 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 172–176 (2020). https://doi.org/10.1109/PDGC50313.2020.9315786
Singh, D., Kumar, V.A.: Comprehensive review of computational dehazing techniques. Arch. Comput. Methods Eng. 26, 1395–1413 (2019). https://doi.org/10.1007/s11831-018-9294-z
Galdran, A.: Image dehazing by artificial multiple-exposure image fusion. Signal Process. 149, 135–147 (2018)
Wang, J., Lu, K., Xue, J., He, N., Shao, L.: Single image dehazing based on the physical model and MSRCR algorithm. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2190–2199 (2018). https://doi.org/10.1109/TCSVT.2017.2728822
Gupta, N., Garg, H., Agarwal, R.: A robust framework for glaucoma detection using CLAHE and EfficientNet. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02114-5
Xin, L., He, Z., Yiu-Ming, C., Xinge, Y., Yuan, Y.T.: Efficient single image dehazing and denoising: an efficient multi-scale correlated wavelet approach. Comput. Vis. Image Understand. 162, 23–33 (2017)
Liu, C., Zhao, J., Shen, Y., et al.: Texture filtering based physically plausible image dehazing. Vis Comput 32, 911–920 (2016). https://doi.org/10.1007/s00371-016-1259-3
Wang, W., Yuan, X., Wu, X., Liu, Y.: Fast image dehazing method based on linear transformation. IEEE Trans. Multimed. 19(6), 1142–1155 (2017). https://doi.org/10.1109/TMM.2017.2652069
Baig, N., Riaz, M.M., Ghafoor, A., Siddiqui, A.M.: Image dehazing using quadtree decomposition and entropy-based contextual regularization. IEEE Signal Process. Lett. 23(6), 853–857 (2016). https://doi.org/10.1109/LSP.2016.2559805
Li, Z., Zheng, J.: Single image de-hazing using globally guided image filtering. IEEE Trans. Image Process. 27(1), 442–450 (2018). https://doi.org/10.1109/TIP.2017.2750418
Ju, M., Ding, C., Ren, W., Yang, Y., Zhang, D., Guo, Y.J.: IDE: image dehazing and exposure using an enhanced atmospheric scattering model. IEEE Trans. Image Process. 30, 2180–2192 (2021). https://doi.org/10.1109/TIP.2021.3050643
Mi, Z., Zhou, H., Zheng, Y., Wang, M.: Single image dehazing via multi-scale gradient domain contrast enhancement. IET Image Process 10(3), 206–214 (2016)
Zheng, M., Qi, G., Zhu, Z., Li, Y., Wei, H., Liu, Y.: Image dehazing by an artificial image fusion method based on adaptive structure decomposition. IEEE Sensors J. 20(14), 8062–8072 (2020). https://doi.org/10.1109/JSEN.2020.2981719
Yingying, Z., Gaoyang, T., Xiaoyan, Z., Jianmin, J., Qi, T.: Haze removal method for natural restoration of images with sky. Neurocomputing 275, 499–510 (2018)
Yin, G., Yijing, S., Qiming, L., Hongyun, L., Jun, L.: Single image dehazing via self-constructing image fusion. Signal Process 167, 107284 (2020)
Agrawal, S.C., Jalal, A.S.: A joint cumulative distribution function and gradient fusion-based method for dehazing of long shot hazy images. J. Vis. Commun. Image Represent. 77, 103087 (2021)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
Dilbag, S., Vijay, K.: Image dehazing using Moore neighborhood-based gradient profile prior. Signal Process. Image Commun. 70, 131–144 (2019)
Bui, T.M., Kim, W.: Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999–1009 (2018). https://doi.org/10.1109/TIP.2017.2771158
Ju, M., Ding, C., Guo, Y.J., Zhang, D.: IDGCP: image dehazing based on gamma correction prior. IEEE Trans. Image Process. 29, 3104–3118 (2020). https://doi.org/10.1109/TIP.2019.2957852
Mandal, S., Rajagopalan, A.N.: Local proximity for enhanced visibility in haze. IEEE Trans. Image Process. 29, 2478–2491 (2020)
Yuan, F., Huang, H.: Image haze removal via reference retrieval and scene prior. IEEE Trans. Image Process. 27(9), 4395–4409 (2018). https://doi.org/10.1109/TIP.2018.2837900
Liu, P., Horng, S., Lin, J., Li, T.: Contrast in haze removal: configurable contrast enhancement model based on dark channel prior. IEEE Trans. Image Process. 28(5), 2212–2227 (2019). https://doi.org/10.1109/TIP.2018.2823424
Bi, G., Ren, J., Fu, T., Nie, T., Chen, C., Zhang, N.: Image dehazing based on accurate estimation of transmission in the atmospheric scattering model. IEEE Photon. J. 9(4), 1–18 (2017). https://doi.org/10.1109/JPHOT.2017.2726107
Zhu, M., He, B., Wu, Q.: Single image dehazing based on dark channel prior and energy minimization. IEEE Signal Process. Lett. 25(2), 174–178 (2018). https://doi.org/10.1109/LSP.2017.2780886
Lu, Z., Long, B., Yang, S.: Saturation based iterative approach for single image dehazing. IEEE Signal Process. Lett. 27, 665–669 (2020). https://doi.org/10.1109/LSP.2020.2985570
Zhu, Q., Mai, J., Shao, L., et al.: A fast single image haze removal algorithm using color attenuation prior. TIP 24(11), 3522–3533 (2015)
Agrawal, S.C., Jalal, A.S.: Distortion-free image dehazing by superpixels and ensemble neural network. Vis. Comput. 38, 781–796 (2022). https://doi.org/10.1007/s00371-020-02049-3
Rathor, S., Jadon, R.S.: Acoustic domain classification and recognition through ensemble based multilevel classification. J Ambient Intell Human. Comput. 10, 3617–3627 (2019). https://doi.org/10.1007/s12652-018-1087-6
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: ECCV (2016)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
He, Z., Vishal, M.P.: Densely connected pyramid dehazing network. In: CVPR, pp. 3194–3203 (2018)
Santra, S., Mondal, R., Chanda, B.: Learning a patch quality comparator for single image dehazing. IEEE Trans. Image Process. 27(9), 4598–4607 (2018). https://doi.org/10.1109/TIP.2018.2841198
Liu, Z., Xiao, B., Alrabeiah, M., Wang, K., Chen, J.: Single image dehazing with a generic model-agnostic convolutional neural network. IEEE Signal Process. Lett. 26(6), 833–837 (2019)
Dong, Y., Jian, S.: Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: ECCV, pp. 702–717 (2018)
Li, L., Dong, Y., Ren, W., Pan, J., Gao, C., Sang, N., Yang, M.H.: Semi-supervised image dehazing. IEEE Trans. Image Process. (2019). https://doi.org/10.1109/TIP.2019.2952690
Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. IEEE Trans. Image Process. 29, 2692–2701 (2020). https://doi.org/10.1109/TIP.2019.2952032
Li, B., Gou, Y., Liu, J.Z., Zhu, H., Zhou, J.T., Peng, X.: Zero-shot image dehazing. IEEE Trans. Image Process. 29, 8457–8466 (2020). https://doi.org/10.1109/TIP.2020.3016134
Chen, Z., Wang, Y., Yang, Y., Liu, D.: PSD: principled synthetic-to-real dehazing guided by physical priors. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7176–7185. https://doi.org/10.1109/CVPR46437.2021.00710
Aditya, M., Harsh, S., Murari, M., Pratik, N.: Domain-aware unsupervised hyperspectral reconstruction for aerial image dehazing. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 413–422 (2021)
Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., Yang, M.-H.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)
Engin, D., Genc, A., Ekenel, H.K.: Cycle-dehaze: enhanced CycleGAN for single image dehazing. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, pp. 938–9388 (2018). https://doi.org/10.1109/CVPRW.2018.00127
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013). https://doi.org/10.1109/TPAMI.2012.213
Hautiere, N., Tarel, J.P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. J. 27(2), 87–95 (2008)
Berman, D., Treibitz, T., Avidan, S.: Single image dehazing using haze-lines. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 720–734 (2020). https://doi.org/10.1109/TPAMI.2018.2882478
Agrawal, S.C., Jalal, A.S.: Dense haze removal by nonlinear transformation. IEEE Trans. Circuits Syst. Video Technol. 32(2), 593–607 (2022). https://doi.org/10.1109/TCSVT.2021.3068625
Raikwar, S.C., Tapaswi, S.: Lower bound on transmission using non-linear bounding function in single image dehazing. IEEE Trans. Image Process. 29, 4832–4847 (2020). https://doi.org/10.1109/TIP.2020.2975909
Dhara, S.K., Roy, M., Sen, D., Biswas, P.K.: Color cast dependent image dehazing via adaptive airlight refinement and non-linear color balancing. IEEE Trans. Circuits Syst. Video Technol. (2020). https://doi.org/10.1109/TCSVT.2020.3007850
Ma, K., Liu, W., Wang, Z.: Perceptual evaluation of single image dehazing algorithms. In: 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, pp. 3600–3604 (2015). https://doi.org/10.1109/ICIP.2015.7351475
Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019). https://doi.org/10.1109/TIP.2018.2867951
Ancuti, C., Ancuti, C.O., De Vleeschouwer, C.: D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 2226–2230 (2016). https://doi.org/10.1109/ICIP.2016.7532754
Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015). https://doi.org/10.1109/TIP.2015.2456502
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013). https://doi.org/10.1109/LSP.2012.2227726
Moorthy, A., Bovik, A.: A modular framework for constructing blind universal quality indices. IEEE Signal Process. Lett. 17, 7 (2009)
Crete-Roffet, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: SPIE (2007)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012). https://doi.org/10.1109/TIP.2012.2191563
Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
Xu, Z., et al.: Strong baseline for single image dehazing with deep features and instance normalization. In: BMVC (2018)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Agrawal, S.C., Agarwal, R. A novel contrast and saturation prior for image dehazing. Vis Comput 39, 5763–5781 (2023). https://doi.org/10.1007/s00371-022-02694-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-022-02694-w