Multi-exposure image fusion aims to generate a single high-dynamic image by
integrating images with different exposures. Existing deep learning-based
multi-exposure image fusion methods primarily focus on spatial domain fusion,
neglecting the global modeling ability of the frequency domain. To effectively
leverage the global illumination modeling ability of the frequency domain, we
propose a novelty perspective on multi-exposure image fusion via the
Spatial-Frequency Integration Framework, named MEF-SFI. Initially, we revisit
the properties of the Fourier transform on the 2D image, and verify the
feasibility of multi-exposure image fusion on the frequency domain where the
amplitude and phase component is able to guide the integration of the
illumination information. Subsequently, we present the deep Fourier-based
multi-exposure image fusion framework, which consists of a spatial path and
frequency path for local and global modeling separately. Specifically, we
introduce a Spatial-Frequency Fusion Block to facilitate efficient interaction
between dual domains and capture complementary information from input images
with different exposures. Finally, we combine a dual domain loss function to
ensure the retention of complementary information in both the spatial and
frequency domains. Extensive experiments on the PQA-MEF dataset demonstrate
that our method achieves visual-appealing fusion results against
state-of-the-art multi-exposure image fusion approaches. Our code is available
at this https URL

通过空频积分框架 (Spatial-Frequency Integration Framework) 提出了一种新颖的多曝光图像融合方法 (MEF-SFI)，通过有效利用频率域的整体光照建模能力，结合空间和频率路径的深度傅里叶变换方法，以及双域损失函数的综合作用，实现了对输入图像的融合，取得了与最先进的多曝光图像融合方法相媲美的视觉效果。