利用扩散先验进行真实世界图像超分辨率

Exploiting Diffusion Prior for Real-World Image Super-Resolution

May 11, 2023

作者: Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C. K. Chan, Chen Change Loy

cs.AI

摘要

我们提出了一种新颖的方法，利用预训练的文本到图像扩散模型中封装的先验知识来进行盲超分辨率（SR）。具体来说，通过使用我们的时间感知编码器，我们可以在不改变预训练合成模型的情况下实现令人满意的恢复结果，从而保留生成先验并最小化训练成本。为了弥补扩散模型固有随机性导致的保真度损失，我们引入了一个可控特征包裹模块，允许用户在推断过程中通过简单调整标量值来平衡质量和保真度。此外，我们开发了一种渐进聚合采样策略，以克服预训练扩散模型的固定尺寸限制，实现对任意尺寸分辨率的适应。通过对我们的方法使用合成和真实基准的全面评估，证明了其优于当前最先进方法的优越性。

English

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we introduce a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches.