DeltaDiff: Reality-Driven Diffusion with AnchorResiduals for Faithful SR

• URL: http://arxiv.org/abs/2502.12567v2
• Date: Wed, 16 Jul 2025 08:33:10 GMT
• Title: DeltaDiff: Reality-Driven Diffusion with AnchorResiduals for Faithful SR
• Authors: Chao Yang, Yong Fan, Qichao Zhang, Cheng Lu, Zhijing Yang,
• Abstract summary: We propose DeltaDiff, a novel frame.work that constrains the difusion process.<n>Our method surpasses state-of-the-art models and generates re-sults with better fidelity.<n>This work establishes a new low-rank constrained par-adigm for applying diffusion models to image reconstruction tasks.
• Score: 10.790771977682763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the transfer application of diffusion models in super-resolu-tion tasks has faced the problem ofdecreased fidelity. Due to the inherent randomsampling characteristics ofdiffusion models, direct application in super-resolu-tion tasks can result in generated details deviating from the true distribution ofhigh-resolution images. To address this, we propose DeltaDiff, a novel frame.work that constrains the difusion process, its essence is to establish a determin-istic mapping path between HR and LR, rather than the random noise disturbanceprocess oftraditional difusion models. Theoretical analysis demonstrates a 25%reduction in diffusion entropy in the residual space compared to pixel-space diffiusion, effectively suppressing irrelevant noise interference. The experimentalresults show that our method surpasses state-of-the-art models and generates re-sults with better fidelity. This work establishes a new low-rank constrained par-adigm for applying diffusion models to image reconstruction tasks, balancingstochastic generation with structural fidelity. Our code and model are publiclyavailable at https://github.com/continueyang/DeltaDiff .

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