BSMEF: Optimized multi-exposure image fusion using B-splines and Mamba

Abstract

In recent years, multi-exposure image fusion has been widely applied to process overexposed or underexposed images due to its simplicity, effectiveness, and low cost. With the development of deep learning techniques, related fusion methods have been continuously optimized. However, retaining global information from source images while preserving fine local details remains challenging, especially when fusing images with extreme exposure differences, where boundary transitions often exhibit shadows and noise. To address this, we propose a multi-exposure image fusion network model, BSMEF, based on B-Spline basis functions and Mamba. The B-Spline basis function, known for its smoothness, reduces edge artifacts and enables smooth transitions between images with varying exposure levels. In BSMEF, the feature extraction module, combining B-Spline and deformable convolutions, preserves global features while effectively extracting fine-grained local details. Additionally, we design a feature enhancement module based on Mamba blocks, leveraging its powerful global perception ability to capture contextual information. Furthermore, the fusion module integrates three feature enhancement methods: B-Spline basis functions, attention mechanisms, and Fourier transforms, addressing shadow and noise issues at fusion boundaries and enhancing the focus on important features. Experimental results demonstrate that BSMEF outperforms existing methods across multiple public datasets.