Recently, deep learning-based methods have dominated image dehazing domain. Although very competitive dehazing performance has been achieved with sophisticated models, effective solutions for extracting useful features are still under-explored. In addition, non-local network, which has made a breakthrough in many vision tasks, has not been appropriately applied to image dehazing. Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper. We start with extracting richer features for dehazing. Specifically, we design a multi-stream feature extraction (MSFE) sub-block, which contains three parallel convolutions with different receptive fields (i.e., $1\times 1$, $3\times 3$, $5\times 5$) for extracting multi-scale features. Following MSFE, we employ an attention sub-block to make the model adaptively focus on important channels/regions. The MSFE and attention sub-blocks constitute our MSFAB. Then, we design a cross non-local block (CNLB), which can capture long-range dependencies beyond the query. Instead of the same input source of query branch, the key and value branches are enhanced by fusing more preceding features. CNLB is computation-friendly by leveraging a spatial pyramid down-sampling (SPDS) strategy to reduce the computation and memory consumption without sacrificing the performance. Last but not least, a novel detail-focused contrastive regularization (DFCR) is presented by emphasizing the low-level details and ignoring the high-level semantic information in the representation space. Comprehensive experimental results demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters.

翻译：近年来，基于深度学习的方法主导了图像去雾领域。尽管通过复杂模型取得了极具竞争力的去雾性能，但有效提取有用特征的解决方案仍待深入探索。此外，在众多视觉任务中取得突破的非局部网络尚未被恰当应用于图像去雾。为此，本文提出一种由多流特征注意力模块（MSFAB）和交叉非局部模块（CNLB）构成的多感受野非局部网络（MRFNLN）。我们从提取更丰富特征入手提升去雾效果。具体地，我们设计了多流特征提取（MSFE）子模块，包含三个并行卷积（感受野分别为$1\times 1$、$3\times 3$、$5\times 5$）以提取多尺度特征。在MSFE之后，我们采用注意力子模块使模型自适应聚焦重要通道/区域。MSFE与注意力子模块共同构成MSFAB。随后设计的交叉非局部模块（CNLB）能够捕获超越查询分支的长程依赖关系。与查询分支使用相同输入源不同，其键分支和值分支通过融合更多前置特征得到增强。CNLB通过采用空间金字塔下采样（SPDS）策略降低计算与内存消耗，且不损失性能，具有计算友好特性。最后，我们提出一种新型聚焦细节的对比正则化（DFCR），通过强调表征空间中的底层细节并忽略高层语义信息实现。综合实验结果表明，所提出的MRFNLN模型在参数少于150万的情况下优于当前最先进的去雾方法。