Image dehazing is a challenging task of restoring a clear image from a haze-polluted image. However, most popular dehazing methods have color shift and overexposure problems owing to the inaccurate estimation of the transmission and atmospheric light in the atmospheric scattering model. In view of the existing problems, this paper proposes a simple but effective dehazing network by learning the residual image between the hazy image and haze-free image. A novel transitional convolution structure is constructed in this network, which contributes to utilizing shallow structure information to enhance the final residual image. Furthermore, to provide images with more realistic color information after dehazing, a novel color difference loss based on CIEDE2000 is designed as one term of the total loss function. In addition, for some real-world images that affect practical applications, a brightness enhancement module is also introduced to restore the luminance of the images. Experiments on synthetic datasets and real-world images demonstrate that the proposed method has clear advantages in both subjective and objective evaluation indicators compared to several existing advanced algorithms.

中文翻译：

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具有过渡卷积层的端到端去雾网络

图像去雾是从雾霾污染的图像中恢复清晰图像的一项具有挑战性的任务。然而，由于大气散射模型中对透射和大气光的估计不准确，大多数流行的去雾方法都存在色移和过度曝光的问题。针对存在的问题，本文通过学习有雾图像和无雾图像之间的残差图像，提出了一种简单但有效的去雾网络。该网络构建了一种新颖的过渡卷积结构，有助于利用浅层结构信息来增强最终残差图像。此外，为了在去雾后为图像提供更真实的颜色信息，设计了一种基于 CIEDE2000 的新型色差损失作为总损失函数的一项。此外，对于一些影响实际应用的真实图像，还引入了亮度增强模块来恢复图像的亮度。在合成数据集和真实世界图像上的实验表明，与现有的几种先进算法相比，所提出的方法在主观和客观评价指标上都具有明显的优势。