Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog's attenuation and airlight (or veiling effect) significantly weaken the background scene information in the input image. To address this problem, we introduce a structure-representation network with uncertainty feedback learning. Specifically, we extract the feature representations from a pre-trained Vision Transformer (DINO-ViT) module to recover the background information. To guide our network to focus on non-uniform fog areas, and then remove the fog accordingly, we introduce the uncertainty feedback learning, which produces the uncertainty maps, that have higher uncertainty in denser fog regions, and can be regarded as an attention map that represents fog's density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying light colors that are possibly present in the input image. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively compared to the state-of-the-art methods in handling dense and non-uniform fog or smoke.

翻译：为了解决这个问题,我们引入了一个结构代表网络,并进行不确定的演示,以便从一个经过预先训练的视野变异器(DINO-VIT)模块中提取我们脱色的产出,以恢复背景资料。为了指导我们的网络关注非统一雾区,然后相应地消除雾,我们引入了不确定性反馈学习方法,该学习方法产生不确定性图,在浓雾区具有更高的不确定性,并且可以被视为代表雾密度和分布不均的注意图。根据不确定性图,我们的反馈网络以迭接方式改进我们脱色的输出。此外,为了处理对大气光色进行估算的吸引力问题,我们利用我们输入图像的灰色版,因为通过不同程度的光色和浓度的图像的处理方式,我们目前不易变的光度和密度的图像的处理方式将降低。