Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.

由于模糊、遮蔽效果和低对比度，朦胧的图像质量低下。为了提高它们的可见性，去雾方法试图将它们恢复到相应的清晰场景，通常是通过更多地关注基于已知地面实况获得准确估计。可以通过客观和主观质量评估来描述的去雾图像的感知质量通常不被考虑。本文提供了去雾图像的质量评估，重点是颜色、图像结构和自然度等方面。考虑了四种图像去雾方法，即对比度有限自适应直方图均衡化 (CLAHE)、暗通道先验和细化 (DCP-R)、感知启发深度去雾网络与细化 (PDR-Net) 和条件生成对抗网络 (CGAN) Pix2pix . 然后对去雾结果进行客观和主观评估，对图像质量进行综合评估。总体而言，Pix2pix客观地展现了最好的结果，在色彩和图像结构的恢复方面表现出色。虽然它在自然度方面优于 DCP-R，但我们的主观评估表明 Pix2pix 也是人类观察者最喜欢的。