Super-Resolution Generative Adversarial Networks based Video Enhancement

TL;DR

This paper presents a novel video super-resolution method based on GANs that incorporates 3D Non-Local Blocks to improve temporal coherence and detail preservation in enhanced videos.

Contribution

It extends SRGAN to handle spatio-temporal data with 3D Non-Local Blocks, enabling better video enhancement with improved coherence and texture.

Findings

Enhanced temporal coherence in videos

Sharper textures and fewer artifacts

Two model variants balancing performance and efficiency

Abstract

This study introduces an enhanced approach to video super-resolution by extending ordinary Single-Image Super-Resolution (SISR) Super-Resolution Generative Adversarial Network (SRGAN) structure to handle spatio-temporal data. While SRGAN has proven effective for single-image enhancement, its design does not account for the temporal continuity required in video processing. To address this, a modified framework that incorporates 3D Non-Local Blocks is proposed, which is enabling the model to capture relationships across both spatial and temporal dimensions. An experimental training pipeline is developed, based on patch-wise learning and advanced data degradation techniques, to simulate real-world video conditions and learn from both local and global structures and details. This helps the model generalize better and maintain stability across varying video content while maintaining the general structure besides the pixel-wise correctness. Two model variants-one larger and one more lightweight-are presented to explore the trade-offs between performance and efficiency. The results demonstrate improved temporal coherence, sharper textures, and fewer visual artifacts compared to traditional single-image methods. This work contributes to the development of practical, learning-based solutions for video enhancement tasks, with potential applications in streaming, gaming, and digital restoration.