Multi-Exposure Image Fusion via Distilled 3D LUT Grid with Editable Mode
This addresses the need for fast, editable image fusion on resource-constrained devices, but it appears incremental as it builds on existing 3D LUT technology.
They tackled the problem of real-time high dynamic range image fusion for ultra-high resolution on handheld devices by introducing a teacher-student network to model uncertainty in 3D LUT grids, achieving competitive efficiency and accuracy.
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.