Image deblurring aims to restore the detailed texture information or structures from blurry images, which has become an indispensable step in many computer vision tasks. Although various methods have been proposed to deal with the image deblurring problem, most of them treated the blurry image as a whole and neglected the characteristics of different image frequencies. In this paper, we present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net introduces the frequency separation module (FSM) into an encoder-decoder network architecture to capture the low- and high-frequency information of image at multiple scales. Then, a cycle-consistency strategy and a contrastive learning module (CLM) are respectively designed to retain the low-frequency information and recover the high-frequency information during deblurring. At last, the features of different scales are fused by a cross-scale feature fusion module (CSFFM). Extensive experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.
翻译:图像分流的目的是从模糊图像中恢复详细的纹理信息或结构,这已成为许多计算机视觉任务中不可或缺的一步,虽然提出了处理图像分流问题的各种方法,但大多数方法将模糊图像作为一个整体处理,忽视了不同图像频率的特征。在本文中,我们介绍了一种图像分流的称作多尺度频率分离网络(MSFS-Net)的新方法。MSFS-Net将频率分离模块(FSM)引入一个编码器分解网络结构,以捕捉多尺度图像的低和高频信息。然后,设计了一个循环连贯战略和对比学习模块(CLM),分别是为了保留低频信息,并在分流过程中恢复高频信息。最后,不同尺度的特点由一个跨尺度的特征聚变模块(CSFFFM)结合。关于基准数据集的广泛实验显示,拟议的网络达到了最先进的性能。