Abstract
The image dehazing is a complicated dilemma to resolve the haze density influence on the object depth. Though many pixel-based or color space-based algorithms are used by many researchers for resolving this issue, due to lack of utilizing the prominent information makes the objective not achieved properly. The main objective if this work is to dehaze an image with fine-tuned parameters. The proposed research work prioritized air light and transmittance extraction (PATE) using a novel dual weighted deep channel and spatial attention (DWDCA)-based model helps to give proper weightage for the color information to restore the prominent color. The loss information calculation framework is proposed in this model to further enhance the output. The performance analysis is done with the benchmark datasets such as i-haze, o-haze and SOTS datasets where the proposed algorithm gives significant improvements in the metrics such as PSNR, SSIM and CIEDE2000 than the state-of-the-art algorithms. The proposed dehazing model with dual weighted channel and spatial attention block effectively preserves the data and improves the vision of the image.
Similar content being viewed by others
References
He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Patt Anal Mach Intell 33(12):2341–2353
Ju M, Gu Z, Zhang D (2017) Single image haze removal based on the improved atmospheric scattering model. Neurocomputing 260:180–191
Li B, Peng X, Wang Z, Xu J, Feng D (2017) AOD-Net: all-in-one dehazing network, In: Proceeding on IEEE International conference on computer vision, pp 4780–4788
Kim G, Ha S, Kwon J (2018) Adaptive patch based convolutional neural network for robust dehazing, ICIP 2018, pp 2845–2849
Ren W et al. (2018) Gated fusion network for single image dehazing, In: IEEE/CVF conference on computer vision and pattern recognition, pp 3253–3261
Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced Pix2pix dehazing network. In: IEEE/CVF Conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, 2019: 8152–8160, doi: https://doi.org/10.1109/CVPR.2019.00835
Yang D, Sun J (2018) Proximal Dehaze-Net: a prior learning-based deep network for single image dehazing. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) In: Computer vision–ECCV 2018. Lecture notes in computer science. Springer, Cham, p 11211
Cai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198
Fattal R (2015) Dehazing using color-lines. ACM Trans Graph 34(1):14
Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International conference on computer vision, Sydney. 617–624, https://doi.org/10.1109/ICCV.2013.82
Qin X, Wang Z, Bai Y, Xie1y X, Jia H (2019) FFA-net: feature fusion attention network for single image dehazing
Mondal R, Santra S, Chanda B (2018) Image dehazing by joint estimation of transmittance and airlight using Bi-directional consistency loss minimized FCN. In: IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), 1033–1041
Kudo Y, Kubota A (2018) Image dehazing method by fusing weighted near-infrared image. IEEE
Tan RT Visibility in bad weather from a single image. In: IEEE conference on computer vision and pattern recognition
Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533
Pei SC, Lee TY (2012) Nighttime haze removal using color transfer pre-processing and dark channel prior, In: 19th IEEE International conference on image processing
Berman D, Avidan S, et al. (2016) Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1674–1682
Fattal R (2008) Single image dehazing. ACM Trans Graphics (TOG) 27(3):72
Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multiscale convolutional neural networks. European conference on computer vision. Springer, Berlin, pp 154–169
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3194–3203
Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B (2016) “Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imag 35(5):1160–1169
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imag 35(5):1207–1216
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag 35(5):1285–1298
Jiang Y, Sun C, Zhao Y, Yang L (2017) Image dehazing using adaptive bi-channel priors on superpixels. Comput Vis Image Underst 165:17–32
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299–1312
Jianxin Wu (2017) Introduction to convolutional neural networks LAMDA Group National Key Lab for Novel Software Technology. Nanjing University China 5(23):495
Ancuti C, Ancuti CO, Vleeschouwer CD (2016) D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. In: 1040 2016 IEEE International Conference on Image Processing (ICIP), 2226–2230
Ancuti C, Ancuti CO, Vleeschouwer CD, Bovik AC (2016) Night-time dehazing by fusion. In: 2016 IEEE international conference on image processing (ICIP), 2256–2260
Suárez PL, Sappa AD, Vintimilla BX, Hammoud RI (2018) Deep learning based single image dehazing. In: IEEE/CVF Conference computer vision pattern recognition workshops (CVPRW). pp 1250–12507 https://doi.org/10.1109/CVPRW.2018.00162
Seif G, Androutsos D (2018) Edge-based loss function for single image super-resolution. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), 1468–1472, doi: https://doi.org/10.1109/ICASSP.2018.8461664.
Wang Z, Bovik AC (2000) A universal image quality index. IEEE Signal Process Lett 9(3):81–84. https://doi.org/10.1109/97.995823
Ancuti CO, Ancuti C, Timofte R, De Vleeschouwer C (2018) I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
Ancuti CO, Ancuti C, Timofte R, De Vleeschouwer C (2018) O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. In: IEEE conference on computer vision and pattern recognition, NTIRE Workshop, NTIRE CVPR'18
Koschmieder H (1924) Theorie der horizontalensichtweite. BeitragezurPhysik der freienAtmosphare 12:3353
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057
Vashishth S, Dasgupta SS, Ray SN, Talukdar P (2018) Dating documents using graph convolution networks. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), 1605–1615
Liu J, Zhang Y (2017) Attention modeling for targeted sentiment. In: Proceedings of the 15th Conference of the European chapter of the association for computational linguistics, Vol 2, pp 572–577
Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1412–1421
Yang M, Tu W, Wang J, Xu F, Chen X (2017) Attention based lstm for target dependent sentiment classification. In: Thirty-First AAAI Conference on Artificial Intelligence
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008
Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803
Wang Y, Huang M, Zhao L, et al. (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606–615
Ancuti CO, Ancuti C, Hermans C, Bekaert P (2010) A fast semi-inverse approach to detect and remove the haze from a single image. In: Computer vision ACCV 2010, Lecture notes in computer science, Springer, Berlin and Heidelberg, Nov. 501–514
Emberton S, Chittka L, Cavallaro A (2015) Hierarchical rank-based veiling light estimation for underwater dehazing. In: Proceedings of the British machine vision conference (BMVC), BMVA Press, September 2015: 125.1–125.12
Kratz L, Nishino K (2009) Factorizing scene albedo and depth from a single foggy image. In: 2009 IEEE 12th international conference on computer vision, Sept. 1701–1708
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems Curran associates, Inc., Vol 25, pp 1097–1105
Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vision 48(3):233–254
Ancuti C, Ancuti CO, Timofte R, Van Gool L, Zhang L, Yang MH, et al. (2018) Ntire 2018 challenge on image dehazing: methods and results. In: The IEEE conference on computer vision and pattern recognition (CVPR) Workshops, June
Santra S, Chanda B (2016) Day/night unconstrained image dehazing. In: 2016 23rd International conference on pattern recognition (ICPR), Dec. 2016: 1406–1411
Tang K, Yang J, Wang J (2014) Investigating haze-relevant features in a learning framework for image dehazing. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR), pp 2995–3002
Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Patt Anal Mach Intell 25(6):713–724
Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization. In: Computer vision and pattern recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE computer society conference on, vol. 1. IEEE, 2001, pp. I–I
Treibitz T, Schechner YY (2009) Polarization: beneficial for visibility enhancement?” In: Computer vision and pattern recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 525–532
Kopf J, Neubert B, Chen B, Cohen M, Cohen-Or D, Deussen O, Uyttendaele M, Lischinski D (2008) Deep photo: model-based photograph enhancement and viewing. ACM Trans Graph (TOG) 27(5):116
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Computer vision and pattern recognition (CVPR), 2016 IEEE conference on, IEEE, pp 2921–2929
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159
Chollet F et al (2015) Keras. https://keras.io
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505
Tal I, Bekerman Y, Mor A, Knafo L, Alon J, Avidan S (2020) Nldnet++: A physics based single image dehazing network. In: 2020 IEEE international conference on computational photography (ICCP), pp 1–10
Li J, Li G, Fan H (2018) Image dehazing using residual-based deep CNN. IEEE Access 6:26831–26842
Golts A, Freedman D, Elad M (2019) Unsupervised single image dehazing using dark channel prior loss. IEEE Trans Image Process 29:2692–2701
Chen D, He M, Fan Q, Liao J, Zhang L, Hou D et al. (2019) Gated context aggregation network for image dehazing and deraining. In: 2019 IEEE winter conference on applications of computer vision (WACV), January, pp 1375–1383
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Sharma G, Wencheng W, Dalal EN (2004) The CIEDE2000 colordifference formula: Implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have 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
Suganthi, M., Akila, C. Prioritized air light and transmittance extraction (PATE) using dual weighted deep channel and spatial attention based model for image dehazing. Pattern Anal Applic 26, 969–985 (2023). https://doi.org/10.1007/s10044-023-01187-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-023-01187-3