Detail Enhancement Multi-Exposure Image Fusion Based on Homomorphic Filtering

Abstract

Due to the large dynamic range of real scenes, it is difficult for images taken by ordinary devices to represent high-quality real scenes. To obtain high-quality images, the exposure fusion of multiple exposure images of the same scene is required. The fusion of multiple images results in the loss of edge detail in areas with large exposure differences. Aiming at this problem, this paper proposes a new method for the fusion of multi-exposure images with detail enhancement based on homomorphic filtering. First, a fusion weight map is constructed using exposure and local contrast. The exposure weight map is calculated by threshold segmentation and an adaptively adjustable Gaussian curve. The algorithm can assign appropriate exposure weights to well-exposed areas so that the fused image retains more details. Then, the weight map is denoised using fast-guided filtering. Finally, a fusion method for the detail enhancement of Laplacian pyramids with homomorphic filtering is proposed to enhance the edge information lost by Laplacian pyramid fusion. The experimental results show that the method can generate high-quality images with clear edges and details as well as similar color appearance to real scenes and can outperform existing algorithms in both subjective and objective evaluations.