结合光场深度估计和大气散射模型的图像去雾方法

目前大部分基于物理模型的图像去雾算法存在复原图像色彩失真和天空边界区域出现光晕效应的问题.为了解决这些问题,本文提出了一种将光场深度计算与大气散射模型相结合的图像去雾方法.该方法通过光场极平面图像计算得到场景深度,将场景深度信息计算所得的透射率与暗通道透射率融合得到最终透射率.同时利用场景深度对天空边界进行判定,单独对天空区域进行处理.在合成雾天图像和真实雾天图像上的实验结果表明,与现有的单幅图像去雾算法相比,本文提出的方法在峰值信噪比以及结构相似性上均有提升.同时对去雾之后的图像的色彩保真度以及光晕效应的抑制方面都取得了较好的结果 .

Image Dehazing Method Based on Light Field Depth Estimation and Atmospheric Scattering Model

Most of the image dehazing algorithms based on physical models have the problems of restoring image color distortion and halo effects in the boundary area of the sky. In this paper,we propose an image dehazing method that based on light field depth calculations with atmospheric scattering models. In this method,the depth of the scene is calculated by the light field epipolar plane image,and the initial transmission calculated by the scene depth information is fused with the dark channel transmission to obtain the final transmission. At the same time,the depth of the scene is used to determine the sky boundary,and the sky area is processed separately. The experimental results on the synthetic haze image and the real haze image show that the proposed method has both improve Peak Signal-to-noise Ratio(PSNR)and Structural Similarity(SSIM)compared with a variety of methods. This method can also get good result in recovery the color of haze image and reduce halo effect after dehazing.