CurveMEF: Multi-exposure fusion via curve embedding network

Abstract

Multi-exposure image fusion (MEF) aims to combine multiple images with different exposures into a single image to improve visual quality and preserve details. This paper proposes a curve embedding network for MEF (CurveMEF), which formulates the MEF task as estimating optimal fusion curves based on state feedback using pixel intensities as state variables. The fusion curve is embedded into CurveNet, a lightweight deep network, as a physical prior constraint. Leveraging the proposed physical informed MEF method, the fusion curve can adaptively adjust the pixel distribution of overexposed and underexposed regions based on the pixel intensities, and distinguish the importance of source images. CurveMEF supports both RGB and luminance channel inputs, demonstrating its flexibility. Experimental results show that CurveMEF achieves competitive performance compared to state-of-the-art methods in both qualitative and quantitative analysis. Moreover, CurveNet requires significantly fewer parameters and training data, enabling fast training and inference. The proposed method delivers high-speed and high-quality fusion while significantly reducing computational, providing an efficient and cost-effective solution. The code is publicly available at: https://github.com/PiratePai/CurveMEF.