  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.
