Feature-based Recognition Framework for Super-resolution Images
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 4 Dec 2021.
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References · 14
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
- 2021High-Performance Large-Scale Image Recognition Without Normalizationcited by 360
- 2018Memory Matching Networks for One-Shot Image Recognitioncited by 65
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