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DEHAZING RESEARCH ON BRIGHTNESS EQUALIZATION MODEL OF DRONE IMAGE

机译:无人机图像亮度均衡模型的脱皮研究

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Due to the rapid development of drone technology, aerial imagery of drones is increasingly used in various fields. However, the aerial image of the drone is highly susceptible to weather conditions during the imaging process. Most aerial images are inevitably affected by fog when they are acquired. Due to the scattering and absorption of the atmosphere, the aerial image of the drone in foggy days has the characteristics of low contrast and unclear scenery. Due to the scattering and absorption of the atmosphere, the aerial image of drone acquired in the foggy environment has the characteristics of low contrast and unclear scenery. The Defogging technology for aerial image of drone can obtain a large amount of useful information in a pictures with low information amount through a certain image processing method, and convert the image with low information amount into a useful image. Therefore, the image processing research carried out for such image degradation caused by natural phenomena has universal practical significance. Aiming at the problem that the aerial image of drone is often affected by haze and the image is blurred and the image quality is degraded, this paper proposes a new model for defogging aerial image of drone. The brightness equalization model is used to improve the degraded image with fog defects. The brightness equalization model obtains the brightness channel of the original image based on the HSI transform. The brightness equalization filter is used to dynamically adjust the brightness to the appropriate interval to achieve the purpose of defogging and then further optimizes the result image by using Gaussian blur and color reshaping. Two images with fog problems were compared, using the brightness equalization model of this paper. And the quality evaluation parameters are selected to evaluate the processing results of the dehazing model. The average value of the images processed by the model is more suitable and the main quality evaluation parameters such as standard deviation and entropy are better than those of the original image.The experimental results show that the brightness equalization model can effectively remove the influence of fog in the aerial image of the drone and improve the visual effect of the image.
机译:由于无人机技术的快速发展,无人机的空中图像越来越多地用于各个领域。然而,在成像过程中,无人机的空中图像对天气状况高度敏感。当收购时,大多数空中图像不可避免地受到雾的影响。由于大气的散射和吸收,有雾天内无人机的航拍图像具有低对比度和景象不明的特点。由于大气的散射和吸收,在有雾环境中获得的无人机的航拍图像具有低对比度和景观不明的特点。无人机的空中图像的Defogging技术可以通过某个图像处理方法在具有低信息量的图像中获得大量有用信息,并将具有低信息量的图像转换为有用的图像。因此,对由天然现象引起的这种图像劣化进行的图像处理研究具有普遍的实际意义。旨在解决无人机的空中图像通常受到雾度的影响并且图像模糊而且图像质量降低,本文提出了一种用于无人机的空中图像的新模型。亮度均衡模型用于改善具有雾缺陷的降级图像。亮度均衡模型基于HSI变换获得原始图像的亮度通道。亮度均衡滤波器用于动态地将亮度调节到适当的间隔,以达到脱模的目的,然后通过使用高斯模糊和颜色重塑来进一步优化结果图像。使用本文的亮度均衡模型进行比较两个具有雾问题的图像。选择质量评估参数以评估去脱色模型的处理结果。由模型处理的图像的平均值更适合,并且标准偏差和熵等主要质量评估参数比原始图像的更好。实验结果表明,亮度均衡模型可以有效地消除雾的影响在无人机的空中图像中,提高图像的视觉效果。

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