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

Computational Visual Media 2020年第3期6卷 307-317页

作者： aman chadha John Britto M.Mani Roja Department of Computer Science Stanford University450 Serra MallStanfordCA 94305USA Department of Computer Science University of Massachusetts AmherstAmherstMA 01003USA Department of Electronics and Telecommunication Engineering University of MumbaiMumbaiMaharashtra 400032India

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 the"naturality"of 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.

关键词： networks resolution projection

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