Dynamic Attention-Guided Diffusion for Image Super-Resolution

• URL: http://arxiv.org/abs/2308.07977v3
• Date: Thu, 7 Mar 2024 15:24:03 GMT
• Title: Dynamic Attention-Guided Diffusion for Image Super-Resolution
• Authors: Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio and Andreas Dengel
• Abstract summary: "You Only Diffuse Areas" (YODA) is a dynamic attention-guided diffusion method for image Super-Resolution (SR) We empirically validate YODA by extending leading diffusion-based methods SR3 and SRDiff. Our experiments demonstrate new state-of-the-art performance in face and general SR across PSNR, SSIM, and LPIPS metrics.
• Score: 10.082751617396474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models in image Super-Resolution (SR) treat all image regions with uniform intensity, which risks compromising the overall image quality. To address this, we introduce "You Only Diffuse Areas" (YODA), a dynamic attention-guided diffusion method for image SR. YODA selectively focuses on spatial regions using attention maps derived from the low-resolution image and the current time step in the diffusion process. This time-dependent targeting enables a more efficient conversion to high-resolution outputs by focusing on areas that benefit the most from the iterative refinement process, i.e., detail-rich objects. We empirically validate YODA by extending leading diffusion-based methods SR3 and SRDiff. Our experiments demonstrate new state-of-the-art performance in face and general SR across PSNR, SSIM, and LPIPS metrics. A notable finding is YODA's stabilization effect by reducing color shifts, especially when training with small batch sizes.

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