We investigate two aspects of network architecture design for dynamic scene deblurring: (1) Learning blur characteristics and their location in dynamic scenes, which corresponds to learning what and where to attend in the channel and spatial axes, respectively. In this regard, we design an attention-adaptive module (AAM), the innovation of which is that it adaptively determines the arrangement of channel and spatial attention modules (i.e., sequentially or in parallel). Ablation experiments verified the effectiveness of the AAM by incorporating it into existing deblurring convolutional neural network (CNN) architectures. (2) Intuitively, geometric variations are widely observed in objects in dynamic scenes because different spatial regions are blurred by different motion kernels. However, owing to the fixed geometric structures in their modules, regular CNNs fail to adapt to these variations. Accordingly, we propose a deformable convolutional module (DCM) to handle geometric variations. Preliminary experiments demonstrated that incorporating the AAM and DCM into existing deblurring models can significantly improve performance. Moreover, it was empirically verified that an encoder–decoder ResBlock network incorporating the proposed modules compares favorably with state-of-the-art methods.

我们研究了用于动态场景去模糊的网络体系结构设计的两个方面：（1）学习模糊特征及其在动态场景中的位置，这分别对应于学习在通道和空间轴上参与什么和在哪里参与。在这方面，我们设计了一个注意力自适应模块（AAM），其创新之处在于它自适应地确定通道和空间注意力模块的排列方式（即顺序或并行）。消融实验通过将AAM合并到现有的去模糊卷积神经网络（CNN）体系结构中，验证了AAM的有效性。（2）直观上，由于不同的运动核模糊了不同的空间区域，因此在动态场景中的对象中广泛观察到几何变化。但是，由于模块中的几何结构固定，常规的CNN无法适应这些变化。因此，我们提出了可变形卷积模块（DCM）来处理几何变化。初步实验表明，将AAM和DCM合并到现有的去模糊模型中可以显着提高性能。此外，通过经验证明，结合了所提出模块的编码器-解码器ResBlock网络与最新方法相比具有优势。