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摘要: 针对现有图像去雾方法因空间上/下文信息丢失而无法准确估计大尺度目标特征, 导致图像结构被破坏或去雾不彻底等问题, 提出一种基于误差回传机制的多尺度去雾网络. 网络由误差回传多尺度去雾组(Error-backward multi-scale dehazing group, EMDG)、门控融合模块(Gated fusion module, GFM)和优化模块组成. 其中误差回传多尺度去雾组包括误差回传模块(Error-backward block, EB)和雾霾感知单元(Haze aware unit, HAU). 误差回传模块度量相邻尺度网络特征图之间的差异, 并将生成的差值图回传至上一尺度, 实现对结构信息和上/下文信息的有效复用; 雾霾感知单元是各尺度子网络的核心, 其由残差密集块(Residual dense block, RDB)和雾浓度自适应检测块(Haze density adaptive detection block, HDADB)组成, 可充分提取局部信息并能够根据雾浓度实现自适应去雾. 不同于已有融合方法直接堆叠各尺度特征, 提出的门控融合模块逐像素学习每个子网络特征图对应的最优权重, 有效避免了干扰信息对图像结构和细节信息的破坏. 再经优化模块, 可得到最终的无雾图像. 在合成数据集和真实数据集上的大量实验表明, 该方法优于目前的主流去雾方法, 尤其是对远景雾气去除效果更佳.Abstract: The existing dehazing methods can not estimate large-scale target features accurately due to the loss of spatial context information, leading to the destruction of image structure and remaining of haze. To solve this problem, we propose a novel multi-scale dehazing network based on error-backward mechanism, which is composed of error-backward multi-scale dehazing group (EMDG), gated fusion module (GFM) and optimization module. Error-backward multi-scale dehazing group consists of error-backward block (EB) and haze aware unit (HAU). With comparing the difference between feature maps of the coarse-scale sub-network and those of the fine-scale sub-network, error-backward block produces an error map and then transmits it to the last coarse-scale sub-network. So the structure and context information can be reused effectively. Haze aware unit is the core of all sub-networks, which consists of residual dense blocks (RDB) and haze density adaptive detection block (HDADB). It helps to extract local information and accomplish adaptive dehazing according to haze density. Differently from the existing fusion-based methods stacking features from different scales directly, the proposed gated fusion module learns the optimal weights of feature maps from different sub-networks, which prevents interferences to destroy image structure and details. The output of optimization module will be the final dehazed image. Extensive experiments on synthetic datasets and real datasets validate the superiority of our proposed network, especially for the haze removal at a distant view.
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Key words:
- Image dehazing /
- deep learning /
- multi-scale network /
- error-backward
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图 1 直接融合策略和误差回传策略示意图
Fig. 1 Illustration of direct-integration strategy and error-backward strategy for multi-scale network
图 2 基于误差回传机制的多尺度去雾网络
Fig. 2 Architecture of multi-scale dehazing network based on error-backward mechanism
图 7 与现有方法在SOTS测试集上去雾结果对比
Fig. 7 Comparisons of dehazing results with state-of-the-art methods on SOTS
图 8 HSTS测试集上与现有方法去雾结果对比
Fig. 8 Comparisons of dehazing results with state-of-the-art methods on HSTS
图 9 与现有方法在真实有雾图像上去雾结果对比
Fig. 9 Comparisons of dehazing results with state-of-the-art methods on real hazy images
表 1 SOTS室内测试集去雾结果的定量比较
Table 1 Qualitative comparisons of dehazing results on SOTS indoor test-set
方法 DCP DehazeNet AODNet EPDN GCANet PSNR (dB) 16.62 21.14 19.06 25.06 30.23 SSIM 0.8179 0.8472 0.8504 0.9232 0.9800 方法 GridDehazeNet PFDN YNet MSBDN 本文方法 PSNR (dB) 32.16 32.68 19.04 33.79 33.83 SSIM 0.9836 0.9760 0.8465 0.9840 0.9834 
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表 2 SOTS室外测试集去雾结果的定量比较
Table 2 Qualitative comparisons of dehazing results on SOTS outdoor test-set
方法 DCP DehazeNet MSCNN AODNet PSNR (dB) 19.13 22.46 22.06 20.29 SSIM 0.8148 0.8514 0.9078 0.8765 方法 EPDN GridDehazeNet YNet 本文方法 PSNR (dB) 22.57 30.86 25.02 31.10 SSIM 0.8630 0.9819 0.9012 0.9765
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表 3 O-Haze数据集去雾结果定量比较
Table 3 Qualitative comparisons of dehazing results on O-Haze data-set
方法 DCP MSCNN AODNet EPDN GCANet GridDehazeNet 本文方法 PSNR (dB) 16.78 17.26 15.03 16.00 16.28 18.92 19.28 SSIM 0.6530 0.6501 0.5394 0.6413 0.6450 0.6721 0.6756
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表 4 HSTS测试集去雾结果的定量比较
Table 4 Qualitative comparisons of dehazing results on HSTS test-set
方法 DCP DehazeNet MSCNN AODNet EPDN YNet CCDID 本文方法 PSNR (dB) 14.84 24.48 18.64 20.55 23.38 18.37 17.22 30.07 SSIM 0.7609 0.9153 0.8168 0.8973 0.9059 0.4725 0.8218 0.9658
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表 5 基于不同模块的网络性能比较
Table 5 Comparisons of network performance based on different modules
模块名称 A B C D E 5个RDB √ √ — — — 9个RDB — — √ √ √ GFM — √ √ √ √ EB — — — √ √ HDADB — — — — √ PSNR (dB) 28.79 29.52 31.53 32.45 33.83
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