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A Parallelizable Global Color Consistency Optimization Algorithm for Multiple Images
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DOI：10.1109/LGRS.2024.3496730.png)
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Abstract
En 中文
The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.
Keywords:
Image color analysis
Mathematical models
Optimization
Remote sensing
Computational modeling
Neural radiance field
Intelligent control
Data models
Cost function
Training
Color correction
image mosaicking
parallel computation
remote sensing
Journal
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Citations:
5.1K