Fidelity-Preserved Diffusion-Based Image Super-Resolution with Guidance

While diffusion-based image super‑resolution models have demonstrated remarkable performance in generating visually appealing images, they are prone to hallucination, often producing image details that appear deceptively realistic but are, in fact, spurious. We attribute this to the probabilistic generative nature of diffusion models, yet we contend that generation can be guided. To this end, this paper proposes a conditioning strategy that guides the diffusion process to preserve the fidelity with the given low-resolution (LR) observation, and consequently prevents hallucinatory detail artifacts. Specifically, we derive a theoretical formulation, enabling a unified conditional posterior score to guide the diffusion generation process, including both the training of the denoising autoencoder and the inference of super-resolved images. During training, the LR observation serves as a condition input to guide the denoising autoencoder, while the noise predicted by the autoencoder is used to calculate the consistency loss against the high-resolution (HR) image, steering the training to be well-aligned with the generation of super-resolved images. During inference, where the HR ground truth is unavailable, we instead pair the LR observation with itself. One serves as an input to the trained autoencoder, whereas the other is utilized to compute the residual against the inferred super-resolved image, thereby guiding the inference process. Experimental results demonstrate that our method effectively suppresses hallucinations, achieving significantly more faithful super-resolved results on digits, text characters, and texture details compared to competing methods.