| Autor: |
Sichao Chen, Zhenfei Li, Dilong Shen, Yunzhu An, Jian Yang, Bin Lv, Guohua Zhou |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neurorobotics, Vol 16 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1662-5218 |
| DOI: |
10.3389/fnbot.2022.1105385 |
| Popis: |
To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
