부산대학교 도서관

Infrared image acquisition is extremely challenging due to the huge spend of manpower and material resources. This paper presents a novel approach for generating paired RGB-infrared images by leveraging feature disentanglement and cross-modality reconstruction. The proposed model effectively extracts unique style features and shared content features from input RGB or infrared images and integrates these features to generate synthetic images corresponding to their respective modalities. This method aims to bridge the gap between different imaging modalities. Experimental evaluations were conducted on the ThermalWorld, LLVIP and SYSU-MM01 datasets, demonstrating the superiority of our method over recent image generation networks in terms of generated infrared image quality. The effectiveness of the model is further evidenced by its superior performance in a pedestrian re-identification task.