论文标题
基于天空区域细分的单图像除算算法
Single Image Dehazing Algorithm Based on Sky Region Segmentation
论文作者
论文摘要
在本文中，提出了一种基于区域分割的混合图像脱落方法，以解决暗通道先验算法在脱落天空区域中的缺点。所提出的方法的初步阶段着重于在有雾图像中的天空和非天空区域的分割，以嵌入式置信度利用了刻薄和边缘检测的优势。在第二阶段，采用了改进的深色通道先验算法来划定非天空区域。最终，天空区域由Dehazenet算法处理，该算法依赖于深度学习卷积神经网络。模拟结果表明，本研究中提出的混合方法解决了与雾图图像中与天空区域相关的颜色失真问题。该方法极大地改善了图像质量指数，包括熵信息，边缘的可见性比，平均梯度和饱和百分比，并具有非常快的计算时间，这很好地表明了该模型的出色性能。
In this paper a hybrid image defogging approach based on region segmentation is proposed to address the dark channel priori algorithm's shortcomings in de-fogging the sky regions. The preliminary stage of the proposed approach focuses on the segmentation of sky and non-sky regions in a foggy image taking the advantageous of Meanshift and edge detection with embedded confidence. In the second stage, an improved dark channel priori algorithm is employed to defog the non-sky region. Ultimately, the sky area is processed by DehazeNet algorithm, which relies on deep learning Convolutional Neural Networks. The simulation results show that the proposed hybrid approach in this research addresses the problem of color distortion associated with sky regions in foggy images. The approach greatly improves the image quality indices including entropy information, visibility ratio of the edges, average gradient, and the saturation percentage with a very fast computation time, which is a good indication of the excellent performance of this model.
