A cross layer graphical neural network based convolutional neural network framework for image dehazing

Reference

• Lien CY, Yang FJ, Huang CC. An efficient image dehazing method. In 2012 Sixth International Conference on Genetic and Evolutionary Computing; IEEE; 2012, August. p. 348–351.
   Google Scholar
• Kponou EA, Wang Z, Wei P. Efficient real-time single-image dehazing based on color cube constraint. In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP); IEEE; 2017, August. p. 106–110.
   Google Scholar
• Xiao J, Zhou J, Lei J, et al. Image hazing algorithm based on generative adversarial networks. IEEE Access. 2019;8:15883–15894. doi:10.1109/ACCESS.2019.2962784
   Google Scholar
• Meng J, Li Y, Liang H, et al. Single-Image dehazing based on two-stream convolutional neural network. J Artif Intel Technol. 2022;2(3):100–110.
   Google Scholar
• Berman D, Treibitz T, Avidan S. Single image dehazing using haze-lines. IEEE Trans Pattern Anal Mach Intell. 2018;42(3):720–734. doi:10.1109/TPAMI.2018.2882478
   PubMed Web of Science ®Google Scholar
• Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process. 2015;24(11):3522–3533. doi:10.1109/TIP.2015.2446191
   PubMed Web of Science ®Google Scholar
• Cheng S, Yang B. An efficient single image dehazing algorithm based on transmission map estimation with image fusion. Eng Sci Technol Int J. 2022;35:101190.
   Google Scholar
• Huo F, Zhu X, Zeng H, et al. Fast fusion-based dehazing with histogram modification and improved atmospheric illumination prior. IEEE Sensors J. 2020;21(4):5259–5270. doi:10.1109/JSEN.2020.3033713
   Web of Science ®Google Scholar
• Zhu Z, Wei H, Hu G, et al. A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Trans Instrum Meas. 2020;70:1–23. doi:10.1109/TIM.2020.2991290
   PubMed Web of Science ®Google Scholar
• Chen J, Yang G, Xia M, et al. HDNet: a dual-stream network with progressive fusion for image hazing detection. J Inform Secur Appl. 2022;69:103271.som.
   Web of Science ®Google Scholar
• Soma P, Jatoth RK. An efficient and contrast-enhanced video de-hazing based on transmission estimation using HSL color model. Vis Comput. 2022;38(7):2569–2580. doi:10.1007/s00371-021-02132-3
   Web of Science ®Google Scholar
• Singh K, Khare V, Agarwal V, et al. A review on GAN based image dehazing. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS); IEEE; 2022, May. p. 1565–1571.
   Google Scholar
• Preethi B, Anuradha C, Harshitha I, et al. Underwater image enhancement and super resolution based on deep CNN method. In 2022 8th International Conference on Smart Structures and Systems (ICSSS); IEEE; 2022, April. p. 01–04.
   Google Scholar
• Parihar AS, Singh K, Ganotra A, et al. Contrast aware image dehazing using generative adversarial network. In 2022 2nd International Conference on Intelligent Technologies (CONIT); IEEE; 2022, June. p. 1–6.
   Google Scholar
• Gao X, Tang P, Cheng Q, et al. Air infrared small target local dehazing based on multiple-factor fusion cascade network. Neural Comput Appl. 2023;35(12):8657–8665.
   Web of Science ®Google Scholar
• Joy AB, Jayasree M. Deep multi-scale residual learning for single image de-hazing. In 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS); IEEE; 2022, June. p. 1–6.
   Google Scholar
• Ayoub A, Naeem EA, El-Shafai W, et al. (2022). Video quality enhancement using an optimized dehazing algorithm.
   Google Scholar
• Yoon S, Cho J. Deep multimodal detection in reduced visibility using thermal depth estimation for autonomous driving. Sensors. 2022;22(14):5084. doi:10.3390/s22145084
   PubMed Web of Science ®Google Scholar
• Guo C, Lian J, Li W. Single image dehazing based on multi-scale channel attention mechanism. In 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA); IEEE; 2022, May. p. 568–573.
   Google Scholar
• Alenezi F, Armghan A, Mohanty SN, et al. Block-greedy and CNN based underwater image dehazing for novel depth estimation and optimal ambient light. Water. 2021;13(23):3470. doi:10.3390/w13233470
   Web of Science ®Google Scholar
• Zheng Z, Ren W, Cao X, et al. Ultra-high-definition image dehazing via multi-guided bilateral learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); IEEE; 2021, June; p. 16180–16189.
   Google Scholar
• Soma P, Jatoth RK. Implementation of a novel, fast and efficient image de-hazing algorithm on embedded hardware platforms. Circuits Syst Signal Process. 2021;40(3):1278–1294. doi:10.1007/s00034-020-01517-4
   Web of Science ®Google Scholar
• Likhitaa PS, Anand R. A comparative analysis of image dehazing using image processing and deep learning techniques. In 2021 6th International Conference on Communication and Electronics Systems (ICCES); IEEE; 2021, July. p. 1611–1616.
   Google Scholar
• Ismail M, Zakir A, Moqa S, et al. Encoder decoder based CNN for single image dehazing with a semi-supervised approach. In 2021 International Conference on Innovative Computing (ICIC); IEEE; 2021, November. p. 1–7.
   Google Scholar
• Zhai X, Rajaram A, Ramesh K. Cognitive model for human behavior analysis. J Interconnect Netw. 2022;22(Supp04):2146013. doi:10.1142/S0219265921460130
   Web of Science ®Google Scholar
• Bi G, Wang L, Chen C, et al. Fast single-image dehazing algorithm based on piecewise transformation in optical model. IEEE Photonics J. 2021;13(2):1–19. doi:10.1109/JPHOT.2021.3071384
   Web of Science ®Google Scholar
• Li W, Wei H, Qi G, et al. A fast image dehazing algorithm for highway tunnel based on artificial multi-exposure image fusion. In IOP Conference Series: Materials Science and Engineering; IOP Publishing; 2020. Vol. 741, No. 1, p. 012038.
   Google Scholar
• Zheng M, Qi G, Zhu Z, et al. Image dehazing by an artificial image fusion method based on adaptive structure decomposition. IEEE Sensors J. 2020;20(14):8062–8072. doi:10.1109/JSEN.2020.2981719
   Web of Science ®Google Scholar
• Zhu Z, Luo Y, Wei H, et al. Atmospheric light estimation based remote sensing image dehazing. Remote Sens (Basel). 2021;13(13):2432. doi:10.3390/rs13132432
   Web of Science ®Google Scholar
• Wang W, Yuan X, Wu X, et al. Fast image dehazing method based on linear transformation. IEEE Trans Multimed. 2017;19(6):1142–1155. doi:10.1109/TMM.2017.2652069
   Web of Science ®Google Scholar
• Galdran A. Image dehazing by artificial multiple-exposure image fusion. Signal Process. 2018;149:135–147. doi:10.1016/j.sigpro.2018.03.008
   Web of Science ®Google Scholar
• Kalaivani K, Kshirsagarr PR, Sirisha Devi J, et al. Prediction of biomedical signals using deep learning techniques. J Intell Fuzzy Syst. 2023;(Preprint):1–14.
   Google Scholar
• Zekrifa DMS, Dhanalakshmi M, Puviarasi R, et al. Optimized controller design for renewable energy systems by using deep reinforcement learning technique. Int J Renew Energ Res (IJRER). 2024;14(1):101–110.
   Google Scholar
• Umaamaheshvari A, Sivasankari K, Suguna N, et al. Optimization technique for optimal location selection based on medical image watermarking on healthcare system. J Intell Fuzzy Syst. 2023;(Preprint):1–11.
   Google Scholar
• Gupta S, Patel N, Kumar A, et al. Adaptive fuzzy convolutional neural network for medical image classification. J Intell Fuzzy Syst. 2023;45(6):1–17.
   Web of Science ®Google Scholar
• Chen S, Fan W, Peter S, et al. Defognet: a single-image dehazing algorithm with cyclic structure and cross-layer connections. Complexity. 2021;2021:1–13.
   Web of Science ®Google Scholar
• Zhou Y, Chen Z, Li P, et al. FSAD-Net: feedback spatial attention dehazing network. IEEE Trans Neural Netw Learn Syst. 2022.
   Web of Science ®Google Scholar
• Manu CM, Sreeni KG. GANID: a novel generative adversarial network for image dehazing. Vis Comput. 2023;39(9):3923–3936.
   Web of Science ®Google Scholar