论文标题
NFResnet：用于DEBLURING的多尺度和U形网络
NFResNet: Multi-scale and U-shaped Networks for Deblurring
论文作者
论文摘要
多尺度和U形网络广泛用于各种图像恢复问题，包括DeBlurring。牢记各种应用程序，我们对这些架构及其对图像造影的影响进行比较。我们还引入了一个名为Nfresblock的新块。它由快速的傅立叶变换层和一系列修改的非线性无线活化块组成。基于这些体系结构和添加，我们介绍了NFResnet和NFResnet+，它们分别是经过修改的多尺度和U-NET体系结构。我们还使用三种不同的损失功能来训练这些体系结构：charbonnier损失，边缘损失和频率重建损失。本文介绍了有关深层视频脱蓝色数据集的广泛实验，以及每个组件的消融研究。所提出的体系结构可实现峰信号与噪声（PSNR）比和结构相似性指数（SSIM）值的显着增加。
Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three different loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.
