基于暗原色先验的图像去雾改进方法

Improved Method of Image Defogging Based on Prior of Dark Primary Color

本文基于大气散射模型,建立了雾天图像复原的关系,以暗通道原理作为基础知识,复原雾天图像.对不符合暗原色先验假设的大片浓雾及天空区域,分析图像失真的原因,通过引入容差参数来修正透射率,防止天空区域的去雾程度过大,从而提高含天空区域图像的复原效果.为了避免大气光强过高,去雾程度加强,本文设置了大气光强的阈值,并采用自动色阶算法对复原后的图像的色调分布进行相应调整,使复原图像更自然,增强其视觉效果.完成图像去雾算法验证流程设计,实现参数可调.通过主观与客观的分析方式对3种算法进行性能验证与分析,证明了改进后的算法优于前两种算法,能够达到明显去雾的效果.

This study establishes the relationship between image restoration and foggy days based on the atmospheric scattering model and restores the images according to the dark channel theory.For large-area dense fog and sky that do not conform to the prior assumption of dark primary color,the reason behind image distortion is analyzed,and the transmittance is corrected by introducing a tolerance parameter.As a result,the fog removal of the sky is not too difficult and the restoration of images with the sky is improved.This study sets an intensity threshold to avoid difficult defogging due to over high atmospheric light intensity.In addition,it adjusts the hue distribution of the restored image by an automatic color scale algorithm for a more natural image.Furthermore,the validation process of the image defogging algorithm is designed to obtain adjustable parameters.The subjective and objective performance verification and analysis of the three algorithms prove that the improved algorithm is better than the first two algorithms in defogging.