结合光场多线索和大气散射模型的去雾算法

Image dehazing algorithm by combining light field multi-cues and atmospheric scattering model

雾天拍摄的图像通常会存在对比度低、图像质量差的问题,而这些退化图像会对计算机视觉的应用产生显著的负面影响。针对这些问题,本文首次提出一种将光场与大气散射模型相结合的图像去雾方法。首先利用光场相机捕获多视角信息的优势提取散焦线索和匹配线索估计雾天图像的深度信息,并利用获取的深度信息计算场景初始透射率。然后利用场景深度信息构建新的权重函数,并将其与1-范数上下文规则化相结合对初始透射率图迭代优化。最后利用大气散射模型对光场中心视角图像进行去雾以获得最终的无雾图像。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,峰值信噪比(PSNR)提高约2d B,结构相似性(SSIM)提高约0.04,本文方法更好地保留了图像的结构信息,同时去雾后的图像较好地保持了图像的色彩信息,能获得更优的图像去雾效果。

Image captured in foggy weather often exhibits low contrast and poor image quality,which may have a negative impact on computer vision applications.Aiming at these problems,we propose an image dehazing algorithm by combining light field technology with atmospheric scattering model.Firstly,taking the advantages of capturing multi-view information from light field camera is used to extracting defocus cues and correspondence cues,which are used to estimating the depth information of hazy images,and use the obtained depth information to calculating the scene’s initial transmission.Then use scene depth information to build a new weight function,and combined it with 1-norm context regularization to optimizing the initial transmission map iteratively.Finally,the central perspective image of hazy light field images is dehazed using atmospheric scattering model to obtain the final dehazed images.Experimental results on synthetic hazy images and real hazy images demonstrate that,compared to existing single image dehazing algorithms,the peak signal to noise ratio get 2 dB improvement and the structural similarity raise about 0.04.Moreover,our approach preserves more fine structural information of images and has faithful color fidelity,thus yielding a superior image dehazing result.