低复杂度图像去雾芯片设计中的下采样暗通道先验估计

Yi-Fan Wu, Chian-Huey Liaw, Yu-Hsuan Lee
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引用次数: 0

摘要

图像去雾是一种将模糊图像恢复为无模糊图像的图像处理技术。航迹估计在图像去雾算法中起着重要的作用。暗信道先验(DCP)是一种有效的航迹预测算法。然而,DCP的分选过程带来了巨大的计算量,限制了其在图像去雾芯片设计中的潜力。为了克服这一局限性,提出了降采样DCP (DS-DCP)算法,为航迹估计提供了一种低复杂度的算法。实验结果表明,DS-DCP算法的计算节省率高达98%,误差仅为0.22%。
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Down-Sampling Dark Channel Prior of Airlight Estimation for Low Complexity Image Dehazing Chip Design
Image dehazing is an image processing technique to restore a hazy image back to hazy-free one. Airlight estimation plays an important role in image dehazing algorithm. Dark Channel Prior (DCP) is an efficient algorithm to predict airlight. However, the sorting process of DCP causes tremendous computation requirement, limiting its potential in image dehazing chip design. To overcome this limitation, Down-sampling DCP (DS-DCP) is proposed to provide a low complexity algorithm for airlight estimation. Experiment results demonstrate that the computation saving ratio (CSR) of DS-DCP is as high as 98%, while keeping error as minor as 0.22%.
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