BDPK: Bayesian Dehazing Using Prior Knowledge
BDPK: Bayesian Dehazing Using Prior Knowledge
复制标题
BDPK：使用先验知识进行贝叶斯去雾
DOI：
10.1109/tcsvt.2018.2869594
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发表时间：
2019-08-01
影响因子：
8.4
通讯作者：
Guo, Y. Jay
中科院分区：
文献类型：
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作者：
Ju, Mingye;Ding, Can;Guo, Y. Jay
大气散射模型（ASM）已广泛应用于模糊图像恢复。然而，一旦输入的模糊图像不能完全满足模型的均匀大气和均匀照明等假设，恢复的反照率可能会偏离真实场景。在本文中，我们打破了这些限制，重新定义了一种更可靠的 ASM（RASM），它对于各种实际场景具有极强的适应性。受益于RASM，基于先验知识，进一步提出了一种简单而有效的贝叶斯去雾算法（BDPK）。我们的策略是将单图像去雾问题转换为最大后验概率问题，该问题可以使用现有先验约束近似为优化函数。为了有效地解决这个优化函数，引入了交替最小化技术，这使我们能够直接恢复场景反照率。对大量具有挑战性的图像进行的实验揭示了 BDPK 在消除雾霾方面的能力，并验证了其在质量和效率方面相对于几种最先进技术的优越性。
Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model's assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable ASM (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.