Real-time image dehazing by superpixels segmentation and guidance filter
Real-time image dehazing by superpixels segmentation and guidance filter
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DOI：
10.1007/s11554-020-00953-4
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发表时间：
2020-03-12
影响因子：
3
通讯作者：
Luo, Bin
中科院分区：
文献类型：
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作者：
Hassan, Haseeb;Bashir, Ali Kashif;Luo, Bin
雾和霾对图像质量有很大影响，为了消除雾和霾，采用了去雾和去雾技术。为此，提出了一种有效的自动除雾方法。对有雾图像进行去雾处理，需要估计两个重要的参数：大气光和透射图。对于大气光估计，使用超像素分割方法来分割输入图像。然后对每个超像素强度求和，并进一步与每个超像素单独进行比较，以提取最大强度超像素。从室外朦胧图像中提取最大强度超像素自动选择朦胧区域（大气光）。因此，我们将提取的最大强度超像素的各个通道强度视为我们提出的算法的大气光。其次，在测量的大气光的基础上，估计初始透射图。透射图通过滚动引导滤波器进一步细化，该滤波器保留了最终去雾输出中的大部分图像信息，例如纹理，结构和边缘。最后，通过大气光和精细透射与雾霾成像模型相结合，产生无雾霾图像。通过对几个公开数据集的详细实验，我们表明，与最先进的模型相比，该模型具有更高的准确性，可以恢复高质量的去雾图像。该模型可用于实时图像处理、实时遥感图像、实时水下图像增强、视频引导交通、户外监控和自动驾驶支持系统等实时应用。
Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems.