Single image dehazing is the key to enhancing image visibility in outdoor scenes, which facilitates human observation and computer recognition. The existing approaches generally utilize a one-shot strategy that indiscriminately applies the same filters to all local regions. However, due to neglecting inhomogeneous illumination and detail distortion, their dehazed results easily suffer from underfiltering or overfiltering across different regions. To tackle this issue, we propose a region-adaptive two-shot network (RATNet) that follows a coarse-to-fine framework. First, a lightweight subnetwork is applied to execute regular global filtering and obtain an initially restored image. Then, a two-branch subnetwork is put forward whose branches separately refine its illumination and detail. Eventually, we derive the final prediction by adaptively aggregating the results after illumination modification and detail restoration, whose region-variant weights are jointly optimized by maximizing the similarity between our fused result and haze-free counterpart. Extensive experiments validate the superiority of our proposed algorithm.

中文翻译：

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通过区域自适应二次网络进行单幅图像去雾

单幅图像去雾是增强室外场景图像可见度的关键，便于人类观察和计算机识别。现有的方法通常采用一次性策略，将相同的过滤器不加选择地应用于所有局部区域。然而，由于忽略了不均匀的照明和细节失真，它们的去雾结果很容易受到不同区域的欠过滤或过过滤的影响。为了解决这个问题，我们提出了一个区域自适应二次网络（RATNet），它遵循从粗到细的框架。首先，应用轻量级子网络执行常规全局过滤并获得初始恢复的图像。然后，提出了一个两分支子网络，其分支分别细化其光照和细节。最终，我们通过自适应地聚合光照修改和细节恢复后的结果来得出最终预测，通过最大化我们的融合结果和无雾对应物之间的相似性来联合优化其区域变量权重。大量实验验证了我们提出的算法的优越性。