Lightweight Multi-Scale Context Aggregation Deraining Network With Artifact-Attenuating Pooling and Activation Functions
Lightweight Multi-Scale Context Aggregation Deraining Network With Artifact-Attenuating Pooling and Activation Functions
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DOI：
10.1109/access.2021.3122450
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Kohei Yamamichi;X. Han
中科院分区：
文献类型：
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作者：
Kohei Yamamichi;X. Han
单图像去雨是许多计算机视觉应用中的基本预处理步骤，用于改善恶劣天气条件下下游高级任务的视觉效果和系统性能。本研究提出了一种新颖的多尺度上下文聚合网络，以有效解决单图像去雨问题。具体来说，我们利用轻量级残差结构子网作为基线架构，在原始尺度上提取精细和详细的纹理上下文，并进一步结合多尺度渐进聚合模块（MPAM）来学习补充的高级上下文，以增强整体去雨网络的建模能力。 MPAM被设计为可在任意网络中使用的即插即用模块，由多尺度卷积块组成，用于学习多个感受野中的各种上下文，然后在具有残差连接的相邻尺度之间进行渐进上下文聚合，这有望同时解开雨图像中场景内容和多个雨层的多尺度结构，并建模更具代表性的上下文以重建干净图像。为了减少 MPAM 中的可学习参数，我们进一步探索了上下文幻觉块来替代多尺度卷积块，并提出了一种轻量级 MPAM。此外，为了特别适应处理具有大量不需要的成分（雨层）的输入雨图像，我们通过考虑周围的空间上下文而不是像素操作来深入研究伪影衰减池化和激活函数，并提出空间上下文感知池化（SCAP）和激活（SCAA）以与我们的去雨网络结合以提高性能。对基准数据集的大量实验表明，我们提出的方法比最先进的除雨方法表现更好。
Single image deraining is a fundamental pre-processing step in many computer vision applications for improving the visual effect and system performance of the downstream high-level tasks in adverse weather conditions. This study proposes a novel multi-scale context aggregation network, to effectively solve the single image deraining problem. Specifically, we exploit a lightweight residual structure subnet as the baseline architecture to extract fine and detailed texture context at the original scale and further incorporate a multi-scale progressive aggregation module (MPAM) to learn the complementary high-level context for enhancing the modeling capability of the overall deraining network. The MPAM, designed as a plug-and-play module to be utilized in the arbitrary network, is composed of multi-scale convolution blocks to learn a wide variety of contexts in multiple receptive fields, and then carries out progressive context aggregation between adjacent scales with residual connections, which is expected to concurrently disentangle the multi-scale structures of scene contents and multiple rain layers in the rainy images, and models more representative contexts for reconstructing the clean image. To reduce the learnable parameters in the MPAM, we further explore a context hallucinate block for replacing the multi-scale convolution block, and propose a lightweight MPAM. Moreover, for being specially adaptive to deal with the input rainy images with a lot of unwanted components (rain layers), we delve into the artifact-attenuating pooling and activation functions via taking into consideration of the surrounding spatial context instead of pixel-wise operation and propose the spatial context-aware pooling (SCAP) and activation (SCAA) for incorporating with our deraining network to boost performance. Extensive experiments on the benchmark datasets demonstrate that our proposed method performs favorably against state-of-the-art deraining approaches.