GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution

TL;DR

This paper introduces GRNN, a recurrent neural network utilizing ghost features to reduce redundancy and improve efficiency in video super-resolution, while also addressing gradient disappearance issues.

Contribution

The paper proposes integrating ghost features with RNNs for VSR, reducing feature redundancy and enhancing detail preservation compared to existing models.

Findings

Improved PSNR and SSIM metrics on benchmark datasets.

Better preservation of texture details in videos.

Reduced feature redundancy in VSR models.

Abstract

Modern video super-resolution (VSR) systems based on convolutional neural networks (CNNs) require huge computational costs. The problem of feature redundancy is present in most models in many domains, but is rarely discussed in VSR. We experimentally observe that many features in VSR models are also similar to each other, so we propose to use "Ghost features" to reduce this redundancy. We also analyze the so-called "gradient disappearance" phenomenon generated by the conventional recurrent convolutional network (RNN) model, and combine the Ghost module with RNN to complete the modeling on time series. The current frame is used as input to the model together with the next frame, the output of the previous frame and the hidden state. Extensive experiments on several benchmark models and datasets show that the PSNR and SSIM of our proposed modality are improved to some extent. Some texture details in the video are also better preserved.