Title: Recurrent Back-Projection Generative Adversarial Network for Video Super Resolution. Abstract: In this paper, we propose a new Video Super Resolution algorithm in an attempt to generate videos that are temporally coherent, spatially detailed, and match human perception. To achieve this, we developed a new generative adversarial network named RBPGAN which is composed of two main components: a generator Network that exceeds other models for producing very high-quality frames, and a discriminator which outperforms others in terms of temporal consistency. The generator of the model uses a reduced recurrent back-projection network that takes a set of neighboring frames and a target frame applies SISR (Single Image Super Resolution) on each frame, and applies MISR (Multiple Image Super Resolution) through an encoder-decoder Back-Projection based approach to concatenate them and produce x4 resolution version of the target frame. The Spatio-temporal discriminator uses triplets of frames and penalizes the generator to generate the desired results. Our contribution results in a model that outperforms earlier work in terms of perceptual similarity and natural flow of frames, while maintaining temporal coherence and high-quality spatial details. The algorithm was tested on different datasets to eliminate bias.