TDDSR: Single-Step Diffusion with Two Discriminators for Super Resolution
<|reference_start|>TDDSR: Single-Step Diffusion with Two Discriminators for Super Resolution: Super-resolution methods are increasingly being specialized for both real-world and face-specific tasks. However, many existing approaches rely on simplistic degradation models, which limits their ability to handle complex and unknown degradation patterns effectively. While diffusion-based super-resolution techniques have recently shown impressive results, they are still constrained by the need for numerous inference steps. To address this, we propose TDDSR, an efficient single-step diffusion-based super-resolution method. Our method, distilled from a pre-trained teacher model and based on a diffusion network, performs super-resolution in a single step. It integrates a learnable downsampler to capture diverse degradation patterns and employs two discriminators, one for high-resolution and one for low-resolution images, to enhance the overall performance. Experimental results demonstrate its effectiveness across real-world and face-specific SR tasks, achieving performance comparable to, or even surpassing, another single-step method, previous state-of-the-art models, and the teacher model.<|reference_end|>
@article{kim2024tddsr:,
    title={TDDSR: Single-Step Diffusion with Two Discriminators for Super
  Resolution},
    author={Sohwi Kim, Tae-Kyun Kim},
    journal={arXiv preprint arXiv:2410.07663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07663},
    primaryClass={eess.IV cs.CV}
}