IPCMoE: Integrating Perceptual Cues with Mixture-of-Experts for Joint Low-Light Image Enhancement and Deblurring

Yuezhou Li, Yuzhen Niu, Huangbiao Xu, Hui Da, Rui Xu, Wenxi Liu

Abstract: Visual perception of nighttime images is often compromised by co-existing low-light and blur degradations. While recent methods have made progress in jointly solving these degradations, the diversity of patterns and intensities in degradation has not been properly considered, leading to inconsistent illumination and unintended artifacts. In response, we propose to integrate perceptual cues with mixture-of-experts (IPCMoE) to achieve flexible processing for low-light blurry images. By exploiting the perceptual cues, we strategically combine dedicated experts with the selective collaboration approach for feature enlightening and texture restoration. To this end, we develop perceptual-integrated MoEs by designing customized routers and task-depended experts. Specifically, the texture memorial MoE is developed to preserve valuable features to restore high-fidelity details, and the enhancement MoE that adaptively integrates enlightening cues and texture cues is designed to formulate the relationship between feature enlightening and texture restoration, thereby achieving dynamic image processing. Extensive experiments show that our method achieves state-of-the-art performance on LOL-Blur and Real-LOL-Blur datasets.

External IDs:doi:10.1145/3746027.3755026