iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks

作     者：Aman Chadha John Britto M.Mani Roja Aman Chadha;John Britto;M.Mani Roja

作者机构：Department of Computer ScienceStanford University450 Serra MallStanfordCA 94305USA Department of Computer ScienceUniversity of Massachusetts AmherstAmherstMA 01003USA Department of Electronics and Telecommunication EngineeringUniversity of MumbaiMumbaiMaharashtra 400032India 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2020年第6卷第3期

页      面：307-317页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Stanford University  SU 

主　　题：networks resolution projection 

摘      要：Recently,learning-based models have enhanced the performance of single-image superresolution(SISR).However,applying SISR successively to each video frame leads to a lack of temporal *** neural networks(CNNs)outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio(PSNR)and structural similarity(SSIM).On the other hand,generative adversarial networks(GANs)offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details,usually seen with CNNs when super-resolving at large upscaling *** present i See Better,a novel GAN-based spatio-temporal approach to video super-resolution(VSR)that renders temporally consistent super-resolution videos.i See Better extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its ***,to improve thenaturalityof the superresolved output while eliminating artifacts seen with traditional algorithms,we utilize the discriminator from super-resolution generative adversarial *** mean squared error(MSE)as a primary loss-minimization objective improves PSNR/SSIM,these metrics may not capture fine details in the image resulting in misrepresentation of perceptual *** address this,we use a four-fold(MSE),perceptual,adversarial,and total-variation loss *** results demonstrate that i See Better offers superior VSR fidelity and surpasses state-of-the-art performance.