HHW-Ego: DSLR-Quality Enhancement for Multi-Source Wearable Ego Imaging

Yunfei Tong, Qianqian SUN, Jixiang Luo, Dell Zhang, Hao Sun, Xuelong Li

Keywords: Wearable Ego Imaging; Multi-Source Dataset; Hyperspectral Enhancement

Abstract: Wearable ego-centric images are now in high demand for scenarios ranging from daily smart glass usage to embodied intelligence. But the image quality is far behind smart phone due to the lacking paired high-quality reference images and the dedicated enhancement systems. To solve this, a customized degradation pipeline is designed to generate paired samples matching wearable camera images, boosting the upper limit of enhancement performance. Besides a two-stage enhancement framework is further built: first, tuning an efficient model on the paired dataset to enhance real wearable images; second, using hyperspectral data to refine color temperature for better quality. Moreover we present HHW-Ego, a multi-source paired dataset integrating with Hyperspectral, High-dynamic range and Wearable Ego-centric data, which includes hundreds of image groups spanning indoor/outdoor and day/night scenes. Experiments show the framework effectively enhances images of varying quality, and matches DSLR camera quality in specific scenarios.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 2588