GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention
arXiv:2111.05014v1
Originality Synthesis-oriented
AI Analysis
This work addresses image quality enhancement for applications like photography or medical imaging, but it appears incremental as it builds on existing GAN and attention techniques.
The paper tackled single image super-resolution by proposing a GAN-based method with dual discriminators and channel attention, resulting in sharper and more pleasing images compared to conventional methods.
Single Image Super-Resolution (SISR) is a very active research field. This paper addresses SISR by using a GAN-based approach with dual discriminators and incorporating it with an attention mechanism. The experimental results show that GDCA can generate sharper and high pleasing images compare to other conventional methods.