Multispectral imaging plays a critical role in a range of intelligent transportation applications, including advanced driver assistance systems (ADAS), traffic monitoring, and night vision. However, accurate visible and thermal (RGB-T) image registration poses a significant challenge due to the considerable modality differences. In this paper, we present a novel joint Self-Correlation and Cross-Correspondence Estimation Framework (SC3EF), leveraging both local representative features and global contextual cues to effectively generate RGB-T correspondences. For this purpose, we design a convolution-transformer-based pipeline to extract local representative features and encode global correlations of intra-modality for inter-modality correspondence estimation between unaligned visible and thermal images. After merging the local and global correspondence estimation results, we further employ a hierarchical optical flow estimation decoder to progressively refine the estimated dense correspondence maps. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming the current state-of-the-art (SOTA) methods on representative RGB-T datasets. Furthermore, it also shows competitive generalization capabilities across challenging scenarios, including large parallax, severe occlusions, adverse weather, and other cross-modal datasets (e.g., RGB-N and RGB-D).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    SC3EF: A Joint Self-Correlation and Cross-Correspondence Estimation Framework for Visible and Thermal Image Registration


    Contributors:
    Tong, Xi (author) / Luo, Xing (author) / Yang, Jiangxin (author) / Li, Xin (author) / Cao, Yanpeng (author)


    Publication date :

    2025-06-01


    Size :

    6668481 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Automatic co-registration of thermal and visible image pairs

    SANCHEZ-MONGE ENRIQUE / FOI ALESSANDRO / KENT MICHAEL et al. | European Patent Office | 2024

    Free access

    AUTOMATIC CO-REGISTRATION OF THERMAL AND VISIBLE IMAGE PAIRS

    SNACHEZ-MONGE ENRIQUE / FOI ALESSANDRO / KENT MICHAEL et al. | European Patent Office | 2021

    Free access

    Correlation Techniques of Image Registration

    Pratt, William K. | IEEE | 1974