Bai Haoran, Pan Jinshan, Xiang Xinguang, Tang Jinhui
IEEE Trans Image Process. 2022;31:1217-1229. doi: 10.1109/TIP.2022.3140609. Epub 2022 Jan 19.
We propose an effective image dehazing algorithm which explores useful information from the input hazy image itself as the guidance for the haze removal. The proposed algorithm first uses a deep pre-dehazer to generate an intermediate result, and takes it as the reference image due to the clear structures it contains. To better explore the guidance information in the generated reference image, it then develops a progressive feature fusion module to fuse the features of the hazy image and the reference image. Finally, the image restoration module takes the fused features as input to use the guidance information for better clear image restoration. All the proposed modules are trained in an end-to-end fashion, and we show that the proposed deep pre-dehazer with progressive feature fusion module is able to help haze removal. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods on the widely-used dehazing benchmark datasets as well as real-world hazy images.
我们提出了一种有效的图像去雾算法，该算法从输入的模糊图像本身中探索有用信息，作为去除雾气的指导。所提出的算法首先使用深度预去雾器生成中间结果，并将其作为参考图像，因为它包含清晰的结构。为了更好地探索生成的参考图像中的指导信息，该算法随后开发了一个渐进式特征融合模块，以融合模糊图像和参考图像的特征。最后，图像恢复模块将融合后的特征作为输入，利用指导信息进行更好的清晰图像恢复。所有提出的模块均以端到端的方式进行训练，并且我们表明，所提出的带有渐进式特征融合模块的深度预去雾器能够帮助去除雾气。大量实验结果表明，在广泛使用的去雾基准数据集以及真实世界的模糊图像上，所提出的算法相对于现有方法具有良好的性能。
