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ICDSR: Integrated Conditional Diffusion Model for Single Image Super-Resolution
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AI
DOI：10.1109/TMM.2025.3639910.png)
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Abstract
En 中文
Diffusion Probabilistic Models (DPMs) have recently demonstrated considerable potential for single image super-resolution (SISR) by utilizing a conditional generation process that transforms Gaussian noise into high-resolution (HR) images based on low-resolution (LR) inputs. Current Image-Conditional DPMs (icDPMs) have demonstrated promising results by leveraging LR images as a condition to guide the generation of HR images. However, icDPMs fail to effectively integrate LR images and other conditional information to generate accurate and natural output. To address this issue, we propose an Integrated Conditional Diffusion Model for Single Image Super-Resolution (ICDSR). Our approach encodes the LR image as a condition to generate the prior feature, simultaneously integrating it with timestep information to establish intermediate constraints. To further enhance these constraints, we designed a multi-scale guidance structure for the U-shaped concatenation of the diffusion model during the integration of conditions. This constraint serves as multi-scale guidance specifically designed for the U-shaped concatenation of the diffusion model during the integration of conditions. Specifically, multi-scale integrated information is injected into the diffusion model basic block, informing about the coarse structure of the sharp image at the intermediate layers with spatially adaptive conditions. Additionally, ICDSR employs a lightweight U-Net to provide initial guidance and leverages the diffusion model to learn residual guidance for faster convergence. Extensive experiments on facial and general benchmarks, including the CelebA and DIV2K datasets, demonstrate that ICDSR surpasses existing methods, achieving state-of-the-art perceptual quality while maintaining competitive distortion metrics.
Keywords:
Diffusion models
single image super-resolution
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