A Review of Remote Sensing Image Dehazing

Hudson, 1975, The military applications of remote sensing by infrared, Proc. IEEE, 63, 104, 10.1109/PROC.1975.9711

Zhang, 2008, The Development of Hyperspectral Remote Sensing and Its Threatening to Military Equipments, Electro. Opt. Technol. Appl., 23, 10

Stevens, M.M. (1988). Application of Remote Sensing to the Assessment of Surface Characteristics of Selected Mojave Desert Playas for Military Purposes. [Ph.D. Thesis, University of Missouri-Rolla].

Wang, F., Zhou, K., Wang, M., and Wang, Q. (2020). The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China. Sensors, 20.

Mancini, F., and Pirotti, F. (2021). Innovations in Photogrammetry and Remote Sensing: Modern Sensors, New Processing Strate-gies and Frontiers in Applications. Sensors, 21.

Liu, K., He, L., Ma, S., Gao, S., and Bi, D. (2018). A Sensor Image Dehazing Algorithm Based on Feature Learning. Sensors, 18.

Jiang, 2021, Robust Visual Saliency Optimization Based on Bidirectional Markov Chains, Cogn. Comput., 13, 69, 10.1007/s12559-020-09724-6

Qu, C., Bi, D.-Y., Sui, P., Chao, A.-N., and Wang, Y.-F. (2017). Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors. Sensors, 17.

Tao, S. (2014). Research on Optical Image Degradation and Compensation Technology Based on Atmospheric Physical Characteristics, Zhejiang University. (In Chinese).

Singh, 2018, A Comprehensive Review of Computational Dehazing Techniques, Arch. Comput. Methods Eng., 26, 1395, 10.1007/s11831-018-9294-z

Yuan, X., Ju, M., Gu, Z., and Wang, S. (2017). An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior. Information, 8.

Yuan, 2019, Single Image Dehazing via NIN-DehazeNet, IEEE Access, 7, 181348, 10.1109/ACCESS.2019.2958607

2018, Single image dehazing using a multilayer perceptron, J. Electron. Imaging, 27, 043022

Chua, 1993, The CNN paradigm, IEEE Trans. Circuits Syst. I Fundam. Theory Appl., 40, 147, 10.1109/81.222795

Goodfellow, 2014, Generative Adversarial Networks, Adv. Neural Inf. Process. Syst., 3, 2672

(2019). An End-to-End Pyramid Convolutional Neural Network for Dehazing, Springer.

Gu, 2017, A Single Image Dehazing Method Using Average Saturation Prior, Math. Probl. Eng., 2017, 6851301, 10.1155/2017/6851301

Ju, 2017, Single image haze removal based on the improved atmospheric scattering model, Neurocomputing, 260, 180, 10.1016/j.neucom.2017.04.034

Nayar, S.K., and Narasimhan, S.G. (1999, January 20–27). Vision in bad weather. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.

Narasimhan, 2008, Vision and the atmosphere, ACM SIGGRAPH ASIA 2008 Courses, 3, 233

Narasimhan, 2008, Contrast restoration of weather degraded images, ACM SIGGRAPH ASIA 2008 Courses, 6, 713

Narasimhan, S.G., and Nayar, S.K. (2001, January 8–14). Removing weather effects from monochrome images. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA.

Ju, 2021, IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model, IEEE Trans. Image Process., 30, 2180, 10.1109/TIP.2021.3050643

Ju, 2018, Gamma-Correction-Based Visibility Restoration for Single Hazy Images, IEEE Signal Process. Lett., 25, 1084, 10.1109/LSP.2018.2839580

Hadjidemetriou, E. (2002). Use of Histograms for Recognition, Columbia University.

Cheng, 2004, A simple and effective histogram equalization approach to image enhancement, Digit. Signal Process., 14, 158, 10.1016/j.dsp.2003.07.002

Kim, 1998, Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering, IEEE Trans. Consum. Electron., 44, 82, 10.1109/30.663733

Kim, 2001, An advanced contrast enhancement using partially overlapped sub-block histogram equalization, IEEE Trans. Circuits Syst. Video Technol., 11, 475, 10.1109/76.915354

Land, 1971, Lightness and Retinex Theory, J. Opt. Soc. Am., 61, 1, 10.1364/JOSA.61.000001

Jobson, 1997, Properties and performance of a center/surround retinex, IEEE Trans. Image Process., 6, 451, 10.1109/83.557356

Finlayson, G.D., Hordley, S.D., and Drew, M.S. (2002). Removing shadows from Images using retinex. Color & Imaging Conference, Society for Imaging Science and Technology.

Rahman, Z.U., Jobson, D.J., and Woodell, G.A. (1996, January 19). Multi-scale retinex for color image. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland.

Jobson, 2004, Retinex processing for automatic image enhancement, J. Electron. Imaging, 13, 100, 10.1117/1.1636183

Jobson, D.J., Rahman, Z.U., and Woodell, G.A. (1996). Retinex Image Processing: Improved Fidelity to Direct Visual Observation. Color and Imaging Conference, Society for Imaging Science and Technology. NASA Langley Technical Report Server.

Jobson, 1997, A multiscale retinex for bridging the gap between color images and the human observation of scenes, IEEE Trans. Image Process., 6, 965, 10.1109/83.597272

Seow, 2006, Ratio rule and homomorphic filter for enhancement of digital colour image, Neurocomputing, 69, 954, 10.1016/j.neucom.2005.07.003

Fries, 2005, Image enhancement by stochastic homomorphic filtering, ICASSP IEEE Int. Conf. Acoust. Speech Signal Process., 6, 625

Wang, X., Ju, M., and Zhang, D. (2018). Automatic hazy image enhancement via haze distribution estimation. Adv. Mech. Eng., 10.

Wu, 2015, The latest research progress of image defogging, Acta Autom. Sin., 41, 221

Ancuti, 2013, Single Image Dehazing by Multi-Scale Fusion, IEEE Trans. Image Process., 22, 3271, 10.1109/TIP.2013.2262284

Shi, 2010, Research on Remote Sensing Image Dehazing Algorithm, Spacecr. Recovery Remote Sens., 6, 50

Huang, 2020, A New Haze Removal Algorithm for Single Urban Remote Sensing Image, IEEE Access, 8, 1, 10.1109/ACCESS.2020.2995591

Chaudhry, 2018, A Framework for Outdoor RGB Image Enhancement and Dehazing, IEEE Geosci. Remote Sens. Lett., 15, 932, 10.1109/LGRS.2018.2814016

Ju, 2018, BDPK: Bayesian Dehazing Using Prior Knowledge, IEEE Trans. Circuits Syst. Video Technol., 29, 2349, 10.1109/TCSVT.2018.2869594

Ju, 2017, Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge, Math. Probl. Eng., 2017, 8190182, 10.1155/2017/8190182

Wang, 2017, Single Image Dehazing Based on the Physical Model and MSRCR Algorithm, IEEE Trans. Circuits Syst. Video Technol., 28, 2190, 10.1109/TCSVT.2017.2728822

Ju, 2017, Single image dehazing via an improved atmospheric scattering model, Vis. Comput., 33, 1613, 10.1007/s00371-016-1305-1

He, 2011, Single Image Haze Removal Using Dark Channel Prior, IEEE Trans. Pattern Anal. Mach. Intell., 33, 2341, 10.1109/TPAMI.2010.168

Xu, H., Guo, J., Liu, Q., and Ye, L. (2012, January 23–25). Fast image dehazing using improved dark channel prior. Proceedings of the 2012 IEEE International Conference on Information Science and Technology, Wuhan, China.

Xie, B., Guo, F., and Cai, Z. (2010, January 13–14). Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex. Proceedings of the 2010 International Conference on Intelligent System Design and Engineering Application, Changsha, China.

Houston, 1979, Sensation and Perception, Int. J. Psychol., 51, 80

Preetham, A.J., Shirley, P., and Smits, B. (1999). A Practical Analytic Model for Daylight, ACM.

He, 2010, Guided Image Filtering, Trans. Petri Nets Other Models Concurr. XV, 6, 1

Berman, D., Treibitz, T., and Avidan, S. (2016, January 27–30). Non-local Image Dehazing. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.

Hartigan, 1979, A K-Means Clustering Algorithm, Appl. Stat., 28, 100, 10.2307/2346830

Berman, 2018, Single Image Dehazing Using Haze-Lines, IEEE Trans. Pattern Anal. Mach. Intell., 42, 720, 10.1109/TPAMI.2018.2882478

Tan, R.T. (2008, January 23–28). Visibility in bad weather from a single image. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.

Fattal, 2008, Single image dehazing, ACM Trans. Graph., 27, 1, 10.1145/1360612.1360671

Wang, 2011, Remote sensing image fog removal technology using DCP, J. Geomat. Sci. Technol., 3, 182

Zheng, 2020, Research on remote sensing image defogging method based on DCP, Geomat. Spat. Inf. Technol., 249, 69

Li, 2021, Speed improvement of aerial image defogging algorithm based on DCP, J. Jilin Univ., 59, 77

Wang, 2015, Patch-Based Dark Channel Prior Dehazing for RS Multi-spectral Image, Chin. J. Electron., 24, 573, 10.1049/cje.2015.07.023

Jiao, L., Shi, Z., and Wei, T. (2012, January 16–18). Fast haze removal for a single remote sensing image using dark channel prior. Proceedings of the 2012 International Conference on Computer Vision in Remote Sensing, Xiamen, China.

Long, 2013, Single Remote Sensing Image Dehazing, IEEE Geosci. Remote. Sens. Lett., 11, 59, 10.1109/LGRS.2013.2245857

Dai, 2017, Remote sensing image defogging method based on DCP, Acta Opt. Sin., 37, 348

Lecun, Y. (1989). Generalization and Network Design Strategies, Connectionism in Perspective Elsevier.

LeCun, 1989, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Comput., 1, 541, 10.1162/neco.1989.1.4.541

LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791

Bouvrie, J. (2006). Notes on Convolutional Neural Networks, MIT. Neural Nets, MIT CBCL Tech Report.

Krizhevsky, 2012, ImageNet Classification with Deep Convolutional Neural Networks, Adv. Neural Inf. Process. Syst., 25, 1097

Pan, P.-W., Yuan, F., Guo, J., and Cheng, E. (2017, January 22–25). Underwater image visibility improving algorithm based on HWD and DehazeNet. Proceedings of the 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, China.

Chen, 2020, PMHLD: Patch Map-Based Hybrid Learning DehazeNet for Single Image Haze Removal, IEEE Trans. Image Process., 29, 6773, 10.1109/TIP.2020.2993407

Cai, 2016, DehazeNet: An End-to-End System for Single Image Haze Removal, IEEE Trans. Image Process., 25, 5187, 10.1109/TIP.2016.2598681

Goodfellow, 2013, Maxout Networks, Comput. Sci., 28, 1319

Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11–13). Deep Sparse Rectifier Neural Networks. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL, USA.

Si, J., Harris, S.L., and Yfantis, E. (2018, January 12–12). A Dynamic ReLU on Neural Network. Proceedings of the 2018 IEEE 13th Dallas Circuits and Systems Conference (DCAS), Dallas, TX, USA.

(2018, January 22–25). Super-Resolution Convolutional Neural Networks Using Modified and Bilateral ReLU. Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC) 2019, Auckland, New Zealand.

Ren, 2019, Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges, Int. J. Comput. Vis., 128, 240, 10.1007/s11263-019-01235-8

Esmaeilzehi, A., Ahmad, M.O., and Swamy, M. (2019, January 22–25). UPDCNN: A New Scheme for Image Upsampling and Deblurring Using a Deep Convolutional Neural Network. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.

Li, Y., Zhang, L., Zhang, Y., Xuan, H., and Dai, Q. (2014, January 7–10). Depth map super-resolution via iterative joint-trilateral-upsampling. Proceedings of the 2014 IEEE Visual Communications and Image Processing Conference, Valletta, Malta.

Dziembowski, A., Grzelka, A., Mieloch, D., Stankiewicz, O., and Domanski, M. (2017, January 22–24). Enhancing view synthesis with image and depth map upsampling. Proceedings of the 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), Poznan, Poland.

Tsuchiya, A., Sugimura, D., and Hamamoto, T. (2017, January 17–20). Depth upsampling by depth prediction. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.

Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017, January 22–29). AOD-Net: All-in-One Dehazing Network. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.

Dong, Y., Liu, Y., Zhang, H., Chen, S., and Qiao, Y. (2020, January 7–12). FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.

Guo, 2021, RSDehazeNet: Dehazing Network with Channel Refinement for Multispectral Remote Sensing Images, IEEE Trans. Geosci. Remote. Sens., 59, 2535, 10.1109/TGRS.2020.3004556

Jiang, H., and Lu, N. (2018). Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images. Remote Sens., 10.

Qin, 2018, Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network with the Residual Architecture, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 1645, 10.1109/JSTARS.2018.2812726

Chen, 2021, Hybrid High-Resolution Learning for Single Remote Sensing Satellite Image Dehazing, IEEE Geosci. Remote Sens. Lett., 30, 1

Mehta, A., Sinha, H., Mandal, M., and Narang, P. (2021, January 5–9). Domain-Aware Unsupervised Hyperspectral Reconstruction for Aerial Image Dehazing. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision, Waikola, HI, USA.

Huang, 2021, Self-filtering image dehazing with self-supporting module, Neurocomputing, 432, 57, 10.1016/j.neucom.2020.11.039

Amintoosi, 2011, Video enhancement through image registration based on structural similarity, Imaging Sci. J., 59, 238, 10.1179/1743131X10Y.0000000014

Singh, 2017, Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform, Imaging Sci. J., 6, 1

Zhou, 2004, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13, 600, 10.1109/TIP.2003.819861

Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., Yuan, L., and Hua, G. (2019, January 7–11). Gated Context Aggregation Network for Image Dehazing and Deraining. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.