Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation. Furthermore, we design a scale-controllable conditioning mechanism that consists of a low-resolution (LR) conditioning network and a scaling factor. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output, which enables the model to accommodate the continuous-resolution requirement. Extensive experiments validate the effectiveness of our IDM and demonstrate its superior performance over prior arts.

翻译：图像超分辨率（SR）因其广泛应用而受到越来越多的关注。然而，当前的SR方法普遍存在过度平滑和伪影问题，且大多仅适用于固定放大倍数。本文提出了一种隐式扩散模型（IDM），用于高保真连续图像超分辨率。IDM将隐式神经表示与去噪扩散模型集成在一个统一的端到端框架中，其中在解码过程中采用隐式神经表示以学习连续分辨率表示。此外，我们设计了一种尺度可控的条件调节机制，该机制由一个低分辨率（LR）条件网络和一个缩放因子组成。缩放因子调节分辨率并相应调整最终输出中LR信息与生成特征的比例，从而使模型能够满足连续分辨率的需求。大量实验验证了我们IDM的有效性，并展示了其相较于现有方法的优越性能。