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- Title
Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator's Night Vision Goggle.
- Authors
Jian, Bo-Lin; Chu, Wen-Lin; Li, Yu-Chung; Yau, Her-Terng
- Abstract
This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator's night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.
- Subjects
IMAGE fusion; MATRIX decomposition; LOW-rank matrices; SPARSE matrices; NIGHT vision; DISCRETE cosine transforms
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 6, p2178
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10062178