Abstract
Camera imaging is one of the most important application areas of computer image and video processing. However, computational cost is usually the main reason preventing many state of the art image processing algorithms from being applied to practical applications including camera imaging. This paper proposes a very light-weight end-to-end CNN network (VLW-Net) for single image haze removal. We proposed a new Inception structure. By combining it with a reformulated atmospheric scattering model, our proposed network is at least 6 times more light-weight than the state-of-the-arts. We conduct the experiments on both synthesized and realistic hazy image dataset, and the results demonstrate our superior performance in terms of network size, PSNR, SSIM and the subjective image quality. Moreover, the proposed network can be seamlessly applied to underwater image enhancement, and we witness obvious improvement by comparing with the state-of-the-arts.
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Liu, C., Tao, L., Kim, YT. (2020). VLW-Net: A Very Light-Weight Convolutional Neural Network (CNN) for Single Image Dehazing. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_37
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DOI: https://doi.org/10.1007/978-3-030-40605-9_37
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