  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). However,
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 image while eliminating artifacts seen with traditional
algorithms, we utilize the discriminator from super-resolution generative
adversarial network (SRGAN). 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 (TV)) loss function. Our results demonstrate that iSeeBetter
offers superior VSR fidelity and surpasses state-of-the-art performance.
