CAJ | 학술논문

在尘雾等恶劣天气条件下，由于大气粒子的散射作用，致使获取的道路图像严重退化，给交通运输带来很大的困难。为了提高道路环境的可视性，文中提出了一种基于WLS的雾天交通图像恢复算法。该算法从大气散射模型出发，首先进行大气光照的估计与白平衡处理，然后结合道路环境的约束，构建WLS框架对大气耗散函数进行估计，从而恢复场景反照率。通过实验分析可知，文中算法能够有效去除图像中雾霾，消除了Halo效应的影响，较好地凸显图像远景的细节信息，实现了交通图像的视见度的提高。
재진무등악렬천기조건하，유우대기입자적산사작용，치사획취적도로도상엄중퇴화，급교통운수대래흔대적곤난。위료제고도로배경적가시성，문중제출료일충기우WLS적무천교통도상회복산법。해산법종대기산사모형출발，수선진행대기광조적고계여백평형처리，연후결합도로배경적약속，구건WLS광가대대기모산함수진행고계，종이회복장경반조솔。통과실험분석가지，문중산법능구유효거제도상중무매，소제료Halo효응적영향，교호지철현도상원경적세절신식，실현료교통도상적시견도적제고。
Images of roads captured by visual surveillance system are usually degraded by scattering due to atmospheric particles such as haze, fog and mist, which could frequently bring great difficulties to the transportation. In this paper, a novel method was proposed based on weighted least squares (WLS) to remove fog from a single input image. The proposed algorithm begins with estimation of atmospheric light and white balance. Then, through the constraint from the road environment, the weighted least squares (WLS) framework was constructed to estimate atmospheric veil, and restore the fog image by inverting the atmospheric scattering model. The experimental results demonstrates that, compared with the existing algorithm, the proposed method can remove the fog effectively, eliminate the Halo effects, obtain good restoration of distant scene details, and thus realize the improvement of traffic image visibility.