Feature-based Recognition Framework for Super-resolution Images
This work addresses a domain-specific issue for applications using super-resolution images, but it is incremental as it builds on existing methods like GANs and recognition networks.
The paper tackles the problem of decreased recognition network performance on super-resolution images by proposing a feature-based recognition network combined with GAN (FGAN), which improves recognition accuracy by extracting more beneficial features, resulting in an increase of over 6% compared to ResNet50 and DenseNet121.
In practical application, the performance of recognition network usually decreases when being applied on super-resolution images. In this paper, we propose a feature-based recognition network combined with GAN (FGAN). Our network improves the recognition accuracy by extracting more features that benefit recognition from SR images. In the experiment, we build three datasets using three different super-resolution algorithm, and our network increases the recognition accuracy by more than 6% comparing with ReaNet50 and DenseNet121.