Image dehazing using autoencoder convolutional neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01780-5.pdf
Reference49 articles.
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3. Badhe MV, Prabhakar LR (2016) A Survey on haze removal using image visibility restoration technique. Inter J Comput Sci Mobile Comput 5(2):96–101
4. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. IEEE Conf on Comput Vision Pattern Recognition (CVPR) 2016:1674–1682. https://doi.org/10.1109/CVPR.2016.185
5. Cai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: an end-to-end system for single image haze removal. IEEE Transact Image Process 25(11):5187–5198. https://doi.org/10.1109/TIP.2016.2598681
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1. OptiMD-3D DCNN: A Framework for Restoring the Haze-Free Images by Image Dehazing Techniques Using Heuristic Approach of Adaptive Lifting Wavelet Transform;Cybernetics and Systems;2023-02-21
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