Multi-Scale Mixture-of-Experts ControlNet for Real-World Movie Scene Image Super-Resolution
Abstract
Abstract Image super-resolution (SR) plays a critical role in enhancing visual quality and improving the performance of downstream vision tasks. However, existing SR methods are predominantly trained and evaluated on small-scale, standardized datasets, which limits their generalization and robustness in complex real-world scenarios. As a representative application domain, movie scenes exhibit high structural complexity and visual diversity, often containing special effects, filters, and other non-natural elements that pose additional challenges for SR models. With the rapid development of the film industry and computer vision, a vast amount of high-quality imagery has become available on the web, offering rich external priors that can potentially enhance SR performance. Motivated by this, we propose a novel reference-based SR framework for movie scenes, termed Multi-Scale Mixture-of-Experts ControlNet (MMoEControl). Our approach first retrieves semantically or structurally similar high-quality images from web-scale data based on features extracted from the low-resolution (LR) input, forming a reference image set. We then design a Multi-Scale Mixture-of-Experts (MMoE) framework built upon an improved ControlNet architecture, which injects the multi-scale reference information into a frozen pre-trained diffusion model to guide the generation of HR outputs. The core contributions of our method include a SR-guided reference image retrieval module and a multi-scale conditional…
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