TDDSR: Single-Step Diffusion with Two Discriminators for Super Resolution

• URL: http://arxiv.org/abs/2410.07663v1
• Date: Thu, 10 Oct 2024 07:12:46 GMT
• Title: TDDSR: Single-Step Diffusion with Two Discriminators for Super Resolution
• Authors: Sohwi Kim, Tae-Kyun Kim,
• Abstract summary: 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. Experimental results demonstrate its effectiveness across real-world and face-specific SR tasks.
• Score: 28.174638880324014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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