Abstract
Single image dehazing based on deep learning techniques has made significant progress. Most existing approaches retrieve haze-free images by estimating the transmission map and global atmospheric light, which are difficult to compute. In this paper, an encoder–decoder-based deep network ReEDNet is designed for single image dehazing. Further, a Cross Feature Aggregation (CrFeAt) block is introduced that utilizes skip connections to preserve the spatial feature to the last layer. ReEDNet is a light network that can be deployed in a resource-constrained environment. A quantitative analysis of the proposed network is performed on the RESIDE dataset. Extensive experiments show that the proposed ReEDNet achieves better performance compared to the state-of-the-art approaches.
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Keshaw, K., Pandey, A., Bhaumik, G., Govil, M.C. (2023). ReEDNet-An Encoder–Decoder Framework for Single Image Dehazing. In: Bhateja, V., Yang, XS., Chun-Wei Lin, J., Das, R. (eds) Intelligent Data Engineering and Analytics. FICTA 2022. Smart Innovation, Systems and Technologies, vol 327. Springer, Singapore. https://doi.org/10.1007/978-981-19-7524-0_22
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DOI: https://doi.org/10.1007/978-981-19-7524-0_22
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