针对现有偏振算法依赖于“天空区域”估计大气参数, 从而受白色目标或高亮区域干扰的不足之处, 提出了一种图像普适性多尺度偏振去雾方法.研究偏振差分图像四叉树空间索引和图像暗通道先验方法, 突破估计模型参数利用天空的局限性, 重建场景深度, 结合大气散射模型复原低频无雾图像; 同时针对目标复原过程中噪声遗留问题, 研究软阈值去噪算法, 结合低频信息重构的透射率, 以梯度增强方式丰富纹理细节, 最后小波重构出清晰图像.实验结果表明, 该算法有效消除了估计大气参数受制于天空区域的局限性, 抑制噪声的影响, 复原的目标更加清晰, 细节方面更为丰富, 算法运行效率方面有较大提高.

In order to solve the problem that the existing polarization algorithms relied on the sky region to estimate the atmospheric parameters is interfered by white targets or highlighted regions, a universal multi-scale polarization dehazing method for image is proposed. The quadratic tree spatial index and image dark channel prior method of polarization difference image are researched, which break through the limitations of the estimated model parameters depending on the sky to reconstruct the scene depth, and restore low-frequency foggless images with atmospheric scattering model. At the same time, the soft threshold denoising algorithm is studied to solve the residual noise problem in the target restoration process, combining with the transmission rate reconstructed by low-frequency information, the texture details are enriched by gradient enhancement. Finally, the clear image is reconstruct through wavelet. The experiment results show that the proposed algorithm effectively eliminates the limitation of estimating the atmospheric parameters subjected to the sky region, and suppresses the influence of noise with the operation efficiency improved greatly. The target in restored image is more clearly, and the details are more abundant.