Survey of super-resolution advances including diffusion and transformers
Survey of super-resolution advances including diffusion and transformers
Hitchhiker’s Guide to Super-Resolution: Introduction and Recent Advances
arXiv paper abstract https://arxiv.org/abs/2209.13131v1
arXiv PDF paper https://arxiv.org/pdf/2209.13131v1.pdf
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.
… 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.
… present a critical discussion on contemporary strategies used in SR, and identify promising yet unexplored research directions.
… complement previous surveys by incorporating the latest developments in the field such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latests evaluation techniques.
… also include several visualizations for the models and methods throughout each chapter in order to facilitate a global understanding of the trends in the field …
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