Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances

arXiv:2209.13131v2 Announce Type: cross
Abstract: With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances, and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We present a critical discussion on contemporary strategies used in SR, …

abstract advances arxiv become challenges cs.cv cs.lg deep learning domain eess.iv evaluation evaluation metrics functions guide however introduction loss metrics research resolution results review type