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Multi-Focus Image Fusion Algorithm in Sensor Networks
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DOI：10.1109/ACCESS.2018.2866020.png)
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Abstract
En 中文
Most imaging systems have a limited depth-of-field in the sensor networks that consist of multiple visual sensors. Due to different object distances, not every object can be clearly imaged by a single sensor. This paper proposes a multi-focus image fusion algorithm in sensor networks. The algorithm combines the advantages of multi-scale analysis and image phase analysis. It uses dynamic window and phase stretch transform (PST) to extract the focused regions of each image accurately and performs multi-resolution analysis of images through the non-sub-sampled shearlet transform. Then, the images are fused according to the local standard deviation of PST feature maps. The results of the simulation experiments prove that our algorithm is effective and outperforms some state-of-the-art algorithms.
Keywords:
Feature extraction
image sensors
image fusion
non-sub-sampled shearlet transform
phase stretch transform
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