当有雾图像中存在大面积明亮区域及景深突变时,使用传统去雾方法处理后的结果容易出现颜色偏移和光晕效应。针对该类问题,提出了一种基于透射率自适应约束修正的图像去雾算法。该算法在大气光值估计阶段使用自动与手动估计相结合的方式,方便使用者对去雾结果进一步根据需求自行调整。关于透射率的估计,首先通过辐射体边界约束求取透射率估计下限以替代传统算法中预先设定的固定数值。然后通过设置阈值判断像素是否在同一景深范围内,并根据强度差值比情况自适应做出相应的修正,以优化透射率估计。结果表明,本文算法能对有雾图像实现较好的图像去雾效果,在恢复清晰图像、增强图像视觉效果和可用性的同时,有效避免图像中明亮区域出现颜色偏移伪影及景深突变处出现光晕效应的问题。

In order to solve the problem of color offset distortion and halo effect when using traditional dehazing algorithms to process the images with large areas of sky and abrupt changes of scene depth, an image dehazing optimization algorithm based on transmittance adaptive constraint correction is proposed. The algorithm combines automatic and manual estimation in the atmospheric light value estimation stage, which is convenient for users to further adjust the dehazing results according to their needs. For the estimation of transmittance, first, the lower limit of the estimation of transmittance is obtained through the boundary constraint of the scene radiance to replace the fixed value set in the traditional algorithm. Then, the threshold value is set to determine whether the pixel is within the same depth of scene, and the corresponding adaptive correction is made according to the intensity difference ratio to optimize the estimation of transmittance. The results show that this algorithm can achieve a better image dehazing effect for the haze image. It can not only restore the clear image, enhance the visual effect, and usability of the images, but also effectively avoid the color deviation artifact in the bright area of the image and halo effect in the sudden changes of depth of areas.