Li Shuoshi, Zhou Yuan, Ren Wenqi, Xiang Wei
IEEE Trans Image Process. 2023;32:6558-6569. doi: 10.1109/TIP.2023.3333564. Epub 2023 Dec 1.
Image dehazing is an effective means to enhance the quality of images captured in foggy or hazy weather conditions. However, existing image dehazing methods are either ineffective in dealing with complex haze scenes, or incurring too much computation. To overcome these deficiencies, we propose a progressive feedback optimization network (PFONet) which is lightweight yet effective for image dehazing. The PFONet consists of a multi-stream dehazing module and a progressive feedback module. The progressive feedback module feeds the output dehazed image back to the intermedia features extracted by the network, thus enabling the network to gradually reconstruct a complex degraded image. Considering both the effectiveness and efficiency of the network, we also design a lightweight hybrid residual dense block serving as the basic feature extraction module of the proposed PFONet. Extensive experimental results are presented to demonstrate that the proposed model outperforms its state-of-the-art single-image dehazing competitors for both synthetic and real-world images.
图像去雾是提高在雾天或霾天条件下拍摄的图像质量的有效手段。然而，现有的图像去雾方法要么在处理复杂的雾霾场景时效果不佳，要么计算量过大。为了克服这些不足，我们提出了一种渐进反馈优化网络（PFONet），它轻量级且对图像去雾有效。PFONet由一个多流去雾模块和一个渐进反馈模块组成。渐进反馈模块将输出的去雾图像反馈到网络提取的中间特征，从而使网络能够逐步重建复杂的退化图像。考虑到网络的有效性和效率，我们还设计了一个轻量级混合残差密集块作为所提出的PFONet的基本特征提取模块。大量实验结果表明，所提出的模型在合成图像和真实世界图像方面均优于其最先进的单图像去雾竞争对手。
