Self-Supervised Deep Blind Video Super-Resolution

TL;DR

This paper introduces a self-supervised deep learning approach for blind video super-resolution that estimates blur kernels and high-resolution videos directly from low-resolution inputs, overcoming limitations of supervised methods.

Contribution

The proposed method is the first to combine self-supervised learning with blind video SR, including auxiliary data generation and optical flow integration for improved results.

Findings

Performs favorably against state-of-the-art methods on benchmarks.

Effectively estimates blur kernels and restores high-resolution videos.

Works well on real-world videos without known degradation models.

Abstract

Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or predefined kernels (e.g., Bicubic kernel) followed by a decimation operation. However, this does not hold for real applications as the degradation process is complex and cannot be approximated by these idea cases well. Moreover, obtaining high-resolution (HR) videos and the corresponding low-resolution (LR) ones in real-world scenarios is difficult. To overcome these problems, we propose a self-supervised learning method to solve the blind video SR problem, which simultaneously estimates blur kernels and HR videos from the LR videos. As directly using LR videos as supervision usually leads to trivial solutions, we develop a simple and effective method to generate auxiliary paired data from original LR videos according to the image formation of video SR, so that the networks can be better constrained by the generated paired data for both blur kernel estimation and latent HR video restoration. In addition, we introduce an optical flow estimation module to exploit the information from adjacent frames for HR video restoration. Experiments show that our method performs favorably against state-of-the-art ones on benchmarks and real-world videos.