A model-based image fusion framework using discrete band-limited shearlets

Abstract

Abstract The limited dynamic range of digital imaging sensors often leads to under- or over-exposed images. While deep learning methods currently dominate multi-exposure image fusion (MEF), they suffer from data dependency and poor interpretability. This paper proposes a novel model-based MEF framework using the Discrete Band-Limited Shearlet Transform (DBLST), which provides superior directional representation compared to traditional wavelets. Our method decomposes source images using DBLST and fuses coefficients according to specifically designed rules for low-frequency and high-frequency components. Extensive experiments demonstrate that the proposed algorithm achieves superior performance to several representative transform-based methods (DWT, NSCT, NSST) in terms of detail preservation and information richness, positioning DBLST as a powerful tool for model-based fusion. The results confirm DBLST as an efficient and interpretable, training-free alternative to data-driven deep learning models for rendering high-dynamic-range images, particularly in scenarios where transparency and reproducibility are prioritized.