Ghost-free multi-exposure image fusion based on dual weights

Le résumé fourni par la source

Multi-exposure image fusion (MEF) is a fundamental technique for High Dynamic Range (HDR) image generation. However, existing MEF approaches commonly suffer from detail loss and motion-induced artifacts, which severely degrade the visual quality of the fused results. To tackle these issues, this paper presents a robust MEF framework based on brightness adaptation and dual-weight decomposition. Specifically, an adaptive brightness weighting strategy is developed that jointly accounts for the global and local luminance characteristics of cross-exposure inputs and dynamically adjusts the corresponding weight distribution. This enables an effective trade-off between local contrast enhancement and global exposure consistency. In parallel, by introducing multi-channel vector gradient weights, the proposed framework can accurately detect edge regions that exhibit similar brightness yet pronounced color differences, thereby providing more precise guidance for structural information extraction. Furthermore, a joint constraint mechanism that integrates structural similarity and brightness consistency is constructed. Through the generation of consistency maps and a reference-frame-guided motion detection and artifact suppression scheme, ghosting is effectively mitigated and the overall fusion quality is substantially improved. Extensive experiments on public datasets demonstrate that the proposed method delivers superior performance in both static and dynamic scenes.

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