With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time. Apart from that, there is a trend to design a manageable and editable algorithm as the different needs of real application scenarios. To tackle these issues, we introduce 3D LUT technology, which can enhance images with ultra-high-definition (UHD) resolution in real time on resource-constrained devices. However, since the fusion of information from multiple images with different exposure rates is uncertain, and this uncertainty significantly trials the generalization power of the 3D LUT grid. To address this issue and ensure a robust learning space for the model, we propose using a teacher-student network to model the uncertainty on the 3D LUT grid.Furthermore, we provide an editable mode for the multi-exposure image fusion algorithm by using the implicit representation function to match the requirements in different scenarios. Extensive experiments demonstrate that our proposed method is highly competitive in efficiency and accuracy.

翻译：随着手持设备成像分辨率的不断提升，现有多曝光图像融合算法难以实时生成超高分辨率的高动态范围图像。此外，针对实际应用场景的不同需求，设计可管控且可编辑的算法已成为趋势。为解决这些问题，我们引入3D LUT技术，该技术可在资源受限设备上实时增强超高清分辨率图像。然而，由于不同曝光率多图像的信息融合具有不确定性，这种不确定性严重考验着3D LUT网格的泛化能力。为解决该问题并确保模型具备稳健的学习空间，我们提出采用师生网络对3D LUT网格的不确定性进行建模。此外，我们通过隐式表示函数为多曝光图像融合算法提供可编辑模式，以适应不同场景的需求。大量实验表明，所提方法在效率与精度方面均具备显著竞争力。