Abstract
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of increase in model size and inference speed, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module in the design to also facilitate efficient video deblurring. Our networks can implicitly model the spatially-varying deblurring process, while dispensing with multi-scale processing and large filters entirely. Extensive qualitative and quantitative comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via reduction in model-size and significant improvements in accuracy and speed, enabling almost real-time deblurring.
Abstract (translated)
本文研究了运动模糊条件下的动态场景去模糊问题。受严重模糊影响的图像恢复需要一个具有大接收场的网络设计，现有网络试图通过简单增加一般卷积层的数量、内核大小或图像处理的比例来实现这一点。然而，以这种方式增加网络容量是以增加模型大小和推理速度为代价的，并且忽略了模糊的不均匀性。我们提出了一种由空间自适应残差学习模块组成的新架构，该模块隐式地发现了导致输入图像中非均匀模糊的空间变化位移，并学会了对滤波器进行调制。该功能由一个自我关注的模块补充，该模块捕获中间功能之间的非本地关系，并增强接收字段。然后，我们在设计中加入时空循环模块，以促进有效的视频去模糊。我们的网络可以隐式地模拟空间变化的去模糊过程，同时完全不需要大规模处理和大型过滤器。在基准动态场景去模糊数据集上与现有技术进行广泛的定性和定量比较，通过减小模型尺寸和显著提高精度和速度，从而实现几乎实时的去模糊，清楚地证明了所提议网络的优越性。
URL
https://arxiv.org/abs/1903.11394
