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Research Articles

Contrast based background and foreground channel prior for single image dehazing

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Pages 599-615 | Received 11 Jul 2022, Accepted 28 Feb 2023, Published online: 11 Mar 2023

References

  • Borkar K, Mukherjee S. Single image dehazing by approximating and eliminating the additional airlight component. Neurocomputing. 2020;400:294–308.
  • Sahu G, et al. Single image dehazing using a new color channel. J Vis Commun Image Represent. 2021;74:103008.
  • Wang S, Zhang L, Wang X. Single image haze removal via attention-based transmission estimation and classification fusion network. Neurocomputing. 2021;447:48–63.
  • Peng S-J, et al. Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization. Neurocomputing. 2021;458:602–614.
  • Lu Y, et al. Accurate estimation of transmission maps for image restoration based on polarimetric parameters and average intensity. Optik. 2020;208:163535.
  • Su YZ, et al. Prior guided conditional generative adversarial network for single image dehazing. Neurocomputing. 2021;423:620–638.
  • Yuan F, et al. A confidence prior for image dehazing. Pattern Recognit. 2021;119:108076.
  • Zou Y, et al. Image haze removal algorithm using a logarithmic guide filtering and multi-channel prior. IEEE Access. 2021;9:11416–11426.
  • Wang C, et al. Weakly supervised single image dehazing. J Vis Commun Image Represent. 2020;72:102897.
  • Mehra A, Narang P, Mandal M. Theianet: towards fast and inexpensive CNN design choices for image dehazing. J Vis Commun Image Represent. 2021;77:103137.
  • Xiao J, et al. Single image dehazing based on learning of haze layers. Neurocomputing. 2020;389:108–122.
  • Chaitanya BSNV, Mukherjee S. Single image dehazing using improved cycleGAN. J Vis Commun Image Represent. 2021;74:103014.
  • Gui B, Zhu Y, Zhen T. Adaptive single image dehazing method based on support vector machine. J Vis Commun Image Represent. 2020;70:102792.
  • Yin S, Wang Y, Yang Y-H. Attentive U-recurrent encoder-decoder network for image dehazing. Neurocomputing. 2021;437:143–156.
  • Wu M, et al. Improvement of dehazing algorithm based on dark channel priori theory. Optik. 2020;206:164174.
  • Tang Q, et al. Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion. Comput Vis Image Underst. 2021;202:103086.
  • Xin Y, et al. Specular reflection image enhancement based on a dark channel prior. IEEE Photon J. 2021;13(1):1–11.
  • Li Y, Jung C, Kim J. Single image depth estimation using edge extraction network and dark channel prior. IEEE Access. 2021;9:112454–112465.
  • Ge X, Tan J, Zhang L. Blind image deblurring using a non-linear channel prior based on dark and bright channels. IEEE Trans Image Process. 2021;30:6970–6984.
  • Zhang Y, et al. Single-image dehazing using extreme reflectance channel prior. IEEE Access. 2021;9:87826–87838.
  • Agrawal SC, Jalal AS. A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images. J Vis Commun Image Represent. 2021;77:103087.
  • Kumar A, Jha RK, Nishchal NK. An improved Gamma correction model for image dehazing in a multi-exposure fusion framework. J Vis Commun Image Represent. 2021;78:103122.
  • Yang M, et al. Pre-processing for single image dehazing. Signal Process Image Commun. 2020;83:115777.
  • He K, Sun J, Tang X. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell. 2010;33(12):2341–2353.
  • Dubok P, et al. Single image dehazing with image entropy and information fidelity. ICIP. 2014: 4037–4041.
  • Ancuti CO, et al. O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2018.
  • Ancuti C, et al. I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, Cham, 2018.
  • Mittal A, Soundararajan R, Bovik AC. Making a “completely blind” image quality analyzer. IEEE Signal Process Lett. 2012;20(3):209–212.
  • Venkatanath N, Praneeth D, Chandrasekhar M, et al. Blind Image Quality Evaluation Using Perception Based Features. In Proceedings of the 21st National Conference on Communications (NCC). Piscataway, NJ: IEEE, 2015.
  • Sheikh HR, Wang Z, Cormack L, et al. LIVE Image Quality Assessment Database Release 2. https://live.ece.utexas.edu/research/quality/.
  • Ancuti CO, et al. Dense-haze: a benchmark for image dehazing with dense-haze and haze-free images. 2019 IEEE international conference on image processing (ICIP). IEEE, 2019.
  • Choi LK, You J, Bovik AC. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process. 2015;24(11):3888–3901.
  • Galdran A. Image dehazing by artificial multiple-exposure image fusion. Signal Process. 2018;149:135–147.
  • Bi G, Zhang Y, Nie T, et al. Single image dehazing based on haze density estimation in different color spaces. OSA Continuum. 2021;4(6):1723–1735.

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