Abstract
Removing non-homogeneous haze from real-world images is a challenging task. Meanwhile, the popularity of high-definition imaging systems and compute-limited smart mobile devices has resulted in new problems, such as the high computational load caused by haze removal for large-size images, or the severe information loss caused by the degradation of both the haze and image downsampling, when applying existing dehazing methods. To address these issues, we propose an isomorphic dual-branch dehazing and super-resolution network for non-homogeneous dehazing of a downsampled hazy image, which produces dehazed and enlarged images with sharp edges and high color fidelity. We quantitatively and qualitatively compare our network with several state-of-the-art dehazing methods under the condition of different downsampling scales. Extensive experimental results demonstrate that our method achieves superior performance in terms of both the quality of output images and the computational load.
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Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (No. 62071201, No. U2031104), and Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010119).
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Kuang, W., Li, Z., Guan, R., Yuan, W., Deng, R., Chen, Y. (2024). Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14451. Springer, Singapore. https://doi.org/10.1007/978-981-99-8073-4_3
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DOI: https://doi.org/10.1007/978-981-99-8073-4_3
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