Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement

Abstract

In order to adapt to the local brightness and contrast of input image sequences, we propose a new weighted average adaptive factor well-exposure weight evaluation scheme. The exposure weights of brighter and darker pixels are determined according to the local average brightness and expected brightness. We find that in the traditional multi-exposure image fusion scheme, the brighter and darker regions of the scene lose many details. To solve this problem, we first propose a standard to determine the brighter and darker regions and then use a fast local Laplacian filter to enhance the image in the region. This paper selects 16 multi-exposure images of different scenes for subjective and objective analysis and compares them with eight existing multi-exposure fusion schemes. The experimental results show that the proposed method can enhance the details appropriately while preserving the details in static scenes and adapting to the input image brightness.

A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion

“…Researchers have conducted extensive research on image enhancement, addressing various problems associated with it and proposing a variety of methods [7]. These methods have many focuses, such as improving visibility in low-light images, controlling brightness inconsistencies in images, and improving results by integrating multiple images.…”