QUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model
This addresses image quality restoration for applications like photography or surveillance, but it appears incremental as it builds on existing diffusion-based methods with specific enhancements.
The paper tackles the problem of image super-resolution in real-world scenarios with unknown and non-uniform degradations, proposing QUSR, which integrates a Quality-Aware Prior and Uncertainty-Guided Noise Generation to produce high-fidelity and high-realism images.
Diffusion-based image super-resolution (ISR) has shown strong potential, but it still struggles in real-world scenarios where degradations are unknown and spatially non-uniform, often resulting in lost details or visual artifacts. To address this challenge, we propose a novel super-resolution diffusion model, QUSR, which integrates a Quality-Aware Prior (QAP) with an Uncertainty-Guided Noise Generation (UNG) module. The UNG module adaptively adjusts the noise injection intensity, applying stronger perturbations to high-uncertainty regions (e.g., edges and textures) to reconstruct complex details, while minimizing noise in low-uncertainty regions (e.g., flat areas) to preserve original information. Concurrently, the QAP leverages an advanced Multimodal Large Language Model (MLLM) to generate reliable quality descriptions, providing an effective and interpretable quality prior for the restoration process. Experimental results confirm that QUSR can produce high-fidelity and high-realism images in real-world scenarios. The source code is available at https://github.com/oTvTog/QUSR.