Abstract
Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.
Highlights
Multi-exposure image fusion (MEF) is an essential technique for integrating image information with different exposure levels, which can more comprehensively understand the scene
To follow the latest development in this field, this paper summarizes the existing MEF methods and presents a literature review
These MEF methods can generally be divided into spatial domain, transform domain, and deep learning
Summary
Brightness in a natural scene usually varies greatly. For example, sunlight is about. Compared with the first way, MEF technology provides a simple, economical, and efficient manner to overcome the contradiction between HDR imaging and a low dynamic range (LDR) display. It avoids the complexity of imaging hardware circuit design and reduces the weight and power consumption of the whole device. MEF is a branch of image fusion, similar to other image fusion tasks [5]; for example, multi-focus image fusion, visible and infrared image fusion, PET and MRI medical image fusion, multispectral and panchromatic remote sensing image fusion, hyperspectral and multispectral remote sensing image fusion, and optical and SAR remote sensing image fusion They combine multidimensional content from multiple-source images to generate high-quality images containing more important information.
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