GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution

• URL: http://arxiv.org/abs/2505.00687v1
• Date: Thu, 01 May 2025 17:48:25 GMT
• Title: GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution
• Authors: Aditya Arora, Zhengzhong Tu, Yufei Wang, Ruizheng Bai, Jian Wang, Sizhuo Ma,
• Abstract summary: GuideSR is a novel single-step diffusion-based image super-resolution (SR) model specifically designed to enhance image fidelity.<n>Our approach consistently outperforms existing methods across various reference-based metrics including PSNR, SSIM, LPIPS, DISTS and FID.
• Score: 15.563111624900865
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we propose GuideSR, a novel single-step diffusion-based image super-resolution (SR) model specifically designed to enhance image fidelity. Existing diffusion-based SR approaches typically adapt pre-trained generative models to image restoration tasks by adding extra conditioning on a VAE-downsampled representation of the degraded input, which often compromises structural fidelity. GuideSR addresses this limitation by introducing a dual-branch architecture comprising: (1) a Guidance Branch that preserves high-fidelity structures from the original-resolution degraded input, and (2) a Diffusion Branch, which a pre-trained latent diffusion model to enhance perceptual quality. Unlike conventional conditioning mechanisms, our Guidance Branch features a tailored structure for image restoration tasks, combining Full Resolution Blocks (FRBs) with channel attention and an Image Guidance Network (IGN) with guided attention. By embedding detailed structural information directly into the restoration pipeline, GuideSR produces sharper and more visually consistent results. Extensive experiments on benchmark datasets demonstrate that GuideSR achieves state-of-the-art performance while maintaining the low computational cost of single-step approaches, with up to 1.39dB PSNR gain on challenging real-world datasets. Our approach consistently outperforms existing methods across various reference-based metrics including PSNR, SSIM, LPIPS, DISTS and FID, further representing a practical advancement for real-world image restoration.

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