Published Paper Details:

Video super-resolution (VSR) aims to enhance the visual quality of low-resolution videos by generating corresponding high-resolution frames. Traditional methods for VSR often suffer from limitations in handling complex motion patterns and preserving temporal consistency across frames. This paper proposes an efficient VSR approach leveraging generative adversarial networks (GANs) and temporal consistency constraints to address these challenges. Our method consists of two key components: a generator network that learns to map low-resolution video frames to their high-resolution counterparts and a discriminator network that distinguishes between generated and real high-resolution frames. Additionally, we introduce temporal consistency constraints to ensure smooth transitions between consecutive frames in the super-resolved video sequence. Experimental results on benchmark datasets demonstrate that our proposed approach achieves state-of-the-art performance in quantitative metrics and visual quality, effectively enhancing low-resolution videos' resolution and temporal coherence