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

Computational Visual Media

Aman Chadha¹  John Britto²  M. Mani Roja³ 

[1] Department of Computer Science, Stanford University, 450 Serra Mall, 94305, Stanford, CA, USA;Department of Computer Science, University of Massachusetts Amherst, 01003, Amherst, MA, USA;Department of Electronics and Telecommunication Engineering, University of Mumbai, 400032, Mumbai, Maharashtra, India;

关键词: super resolution;    video upscaling;    frame recurrence;    optical flow;    generative adversarial networks;    convolutional neural networks;   

DOI  :  10.1007/s41095-020-0175-7

来源: Springer

【 摘 要 】

Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional 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 factors. We present iSeeBetter, a novel GAN-based spatio-temporal approach to video super-resolution (VSR) that renders temporally consistent super-resolution videos. iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Furthermore, to improve the “naturality” of the super-resolved output while eliminating artifacts seen with traditional algorithms, we utilize the discriminator from super-resolution generative adversarial network. Although 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 quality. To address this, we use a four-fold (MSE, perceptual, adversarial, and total-variation loss function. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.

【 授权许可】

CC BY