{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:11:24Z","timestamp":1767982284469,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11554-024-01574-x","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T11:01:09Z","timestamp":1731668469000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An energy-efficient dehazing neural network accelerator based on E$$^2$$AOD-Net"],"prefix":"10.1007","volume":"21","author":[{"given":"Zhihao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Gaoming","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zhenmin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qinran","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Wenyao","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiaolei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"1574_CR1","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z. et al.: Aod-net: All-in-one dehazing network. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 4780\u20134788 (2017)","DOI":"10.1109\/ICCV.2017.511"},{"key":"1574_CR2","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"1574_CR3","doi-asserted-by":"crossref","unstructured":"Gholami, A., Kim, S., Dong, Z. et al.: A survey of quantization methods for efficient neural network inference. arXiv:2103.13630 (2021)","DOI":"10.1201\/9781003162810-13"},{"key":"1574_CR4","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, J., Yao, S. et al.: Going deeper with embedded FPGA platform for convolutional neural network. In Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA \u201916, pp. 26\u201335, New York, NY, USA (2016). Association for Computing Machinery","DOI":"10.1145\/2847263.2847265"},{"key":"1574_CR5","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Liang, Y. Lu, L. et al.: Exploring heterogeneous algorithms for accelerating deep convolutional neural networks on FPGAs. In: 2017 54th ACM\/EDAC\/IEEE Design Automation Conference (DAC), pp. 1\u20136 (2017)","DOI":"10.1145\/3061639.3062244"},{"key":"1574_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/TCAD.2017.2705069","volume":"1","author":"K Guo","year":"2018","unstructured":"Guo, K., Sui, L., Qiu, J., et al.: Angel-eye: a complete design flow for mapping cnn onto embedded FPGA. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 1, 35\u201347 (2018)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circ. Syst."},{"key":"1574_CR7","doi-asserted-by":"crossref","unstructured":"Koranga, P., Kumawat, S.: A review on comparison of different techniques of image dehazing. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1\u20136 (2020)","DOI":"10.1109\/ICRAIE51050.2020.9358387"},{"issue":"11","key":"1574_CR8","doi-asserted-by":"publisher","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xiangmin, X., Jia, K., et al.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187\u20135198 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"1574_CR9","doi-asserted-by":"publisher","first-page":"8968","DOI":"10.1109\/TIP.2021.3116790","volume":"30","author":"H Ullah","year":"2021","unstructured":"Ullah, H., Muhammad, K., Irfan, M., et al.: Light-dehazenet: a novel lightweight cnn architecture for single image dehazing. IEEE Trans. Image Process. 30, 8968\u20138982 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1574_CR10","unstructured":"Zhang, C.L., Xu, R., Wang, Y.J., et al.: FPGA-based video real time dehazing algorithm and hardware implementation[J]. Semiconductor Optoelectron. 42(2), 264\u2013268, 274 (2021)"},{"issue":"10","key":"1574_CR11","first-page":"3184","volume":"42","author":"G Wang","year":"2022","unstructured":"Wang, G., Jian, C., Xiang, Q.: Hardware reconstruction acceleration method of convolutional neural network-based single image dehazing model. J. Comput. Appl. 42(10), 3184\u20133190 (2022)","journal-title":"J. Comput. Appl."},{"key":"1574_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Sun, J., Tang, X.: \"Single image haze removal using dark channel prior. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 1956\u20131963 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206515","DOI":"10.1109\/CVPR.2009.5206515"},{"key":"1574_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Dongdong, He, Mingming, Fan, Qingnan, et al.: Lu\u00a0Yuan, and Gang Hua. Gated context aggregation network for image dehazing and deraining. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1375\u20131383 (2019)","DOI":"10.1109\/WACV.2019.00151"},{"key":"1574_CR14","doi-asserted-by":"crossref","unstructured":"Bhola, Akshay, Sharma, Teena, Verma, Nishchal\u00a0K.: Dcnet: Dark channel network for single-image dehazing. Mach. Vision Appl., 32(3) (may 2021)","DOI":"10.1007\/s00138-021-01173-x"},{"key":"1574_CR15","doi-asserted-by":"crossref","unstructured":"Du, Gaoming, Wu, Jiting, Cao, Hongfang, et al.: A real-time effective fusion-based image dehazing architecture on FPGA. ACM Trans. Multimedia Comput. Commun. Appl., 17(3) (jul 2021)","DOI":"10.1145\/3446241"},{"key":"1574_CR16","doi-asserted-by":"publisher","DOI":"10.27389\/d.cnki.gxadu.2022.001996","author":"C Kezheng","year":"2022","unstructured":"Kezheng, C.: Research on real image dehazing algorithm based on convolution neural network and hardware acceleration technology. Xidian University (2022). https:\/\/doi.org\/10.27389\/d.cnki.gxadu.2022.001996","journal-title":"Xidian University"},{"key":"1574_CR17","doi-asserted-by":"crossref","unstructured":"Alaeddine, Hmidi, Jihene, Malek, Khemaja, Maha: An efficient deep network in network architecture for image classification on FPGA accelerator. In 2021 International Conference on Cyberworlds (CW), pages 72\u201377, (2021)","DOI":"10.1109\/CW52790.2021.00018"},{"key":"1574_CR18","doi-asserted-by":"crossref","unstructured":"Zhan, Canyu, Zhao, Bingjie, Zhang, Huangliang, et al.: Design and implementation of FPGA-based hardware acceleration system for target detection. In 2022 China Automation Congress (CAC), pages 553\u2013559 (2022)","DOI":"10.1109\/CAC57257.2022.10055324"},{"key":"1574_CR19","doi-asserted-by":"crossref","unstructured":"An, Sheng, Deng, Jijie: Low-resource cnn accelerator based on FPGA. In 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), volume\u00a05, pages 1229\u20131233 (2022)","DOI":"10.1109\/IMCEC55388.2022.10019956"},{"key":"1574_CR20","unstructured":"De\u00a0Vleeschouwer Cosmin\u00a0Ancuti, Christophe, Ancuti, Codruta O.: D-hazy: A dataset to evaluate quantitatively dehazing algorithms. In IEEE International Conference on Image Processing (ICIP), ICIP\u201916, (2016)"},{"key":"1574_CR21","doi-asserted-by":"crossref","unstructured":"Ancuti, Codruta\u00a0O., Ancuti, Cosmin, et al.: NH-HAZE: an image dehazing benchmark with non-homogeneous hazy and haze-free images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE CVPR 2020, (2020)","DOI":"10.1109\/CVPRW50498.2020.00230"},{"issue":"1","key":"1574_CR22","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2019","unstructured":"Li, B., Ren, W., Dengpan, F., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492\u2013505 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"1574_CR23","doi-asserted-by":"crossref","unstructured":"Tarel, Jean-Philippe, Hauti\u00e8re, Nicolas, Cord, Aur\u00e9lien, Halmaouiet, Houssam, et al.: Improved visibility of road scene images under heterogeneous fog. 2010 IEEE Intelligent Vehicles Symposium, pages 478\u2013485, (2010)","DOI":"10.1109\/IVS.2010.5548128"},{"key":"1574_CR24","unstructured":"Kuhn, Lorenz, Lyle, Clare, Gomez, Aidan\u00a0N, et al.: Robustness to pruning predicts generalization in deep neural networks. ArXiv, abs\/2103.06002 (2021)"},{"key":"1574_CR25","unstructured":"Fahim, Farah, Hawks, Benjamin, Herwig, Christian, et al.: hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices, (2021)"},{"key":"1574_CR26","doi-asserted-by":"crossref","unstructured":"Umuroglu, Y., Fraser, N.J., Gambardella, G. et al.: Finn: A framework for fast, scalable binarized neural network inference. In: Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA \u201917. ACM (2017)","DOI":"10.1145\/3020078.3021744"},{"key":"1574_CR27","unstructured":"Liu, Y., Zhao, G.: P Pad-net: A perception-aided single image dehazing network. arXiv:1805.03146 (2018)"},{"issue":"1","key":"1574_CR28","first-page":"4945214","volume":"2020","author":"W Qian","year":"2020","unstructured":"Qian, W., Zhou, C., Zhang, D.: Faod-net: A fast aod-net for dehazing single image. Math. Probl. Eng. 2020(1), 4945214 (2020)","journal-title":"Math. Probl. Eng."},{"key":"1574_CR29","doi-asserted-by":"crossref","unstructured":"Teng, P., Du, G., Li Z. et al.: High-speed hardware accelerator based on brightness improved by light-dehazenet. J. Real-Time Image Process. 21(3) (2024)","DOI":"10.1007\/s11554-024-01464-2"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01574-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01574-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01574-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:18:52Z","timestamp":1732799932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01574-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1574"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01574-x","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"5 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"197"}}