NFResNet: Multi-scale and U-shaped Networks for Deblurring

Autor:	Mittal, Tanish, Agrawal, Preyansh, Pahwa, Esha, Makwana, Aarya
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Due to limitations in GPU Compute, We weren't able to test the paper on the popularly used GoPro Dataset which is mostly used for testing image deblurring problems. Afterwards the submission on Arxiv, We observed that we missed comparison of our results with some State-of-the-art papers like ARVo & Gated Spatio-Temporal Attention-Guided Video Deblurring
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.05909 Zobrazit plný text záznamu View this record from Arxiv