- Tytuł:
- Deblurring approach for motion camera combining FFT with α-confidence goal optimization
- Autorzy:
-
Huang, Lve
Wu, Lushen
Xiao, Wenyan
Peng, Qingjin - Powiązania:
- https://bibliotekanauki.pl/articles/174045.pdf
- Data publikacji:
- 2020
- Wydawca:
- Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
- Tematy:
-
image deblurring
fast motion camera
confidence goal optimization
fast Fourier transform
high-railway defect detection - Opis:
- Sharp images ensure success in the object detection and recognition from state-of-art deep learning methods. When there is a fast relative motion between the camera and the object being imaged during exposure, it will necessarily result in blurred images. To deblur the images acquired under the camera motion for high-quality images, a deblurring approach with relatively simple calculation is proposed. An accurate estimation method of point spread function is firstly developed by performing the Fourier transform twice. Artifacts caused by image direct deconvolution are then reduced by predicting the image boundary region, and the deconvolution model is optimized by an α-confidence statistics algorithm based on the greyscale consistency of the image adjacent columns. The proposed deblurring approach is finally carried out on both the synthetic-blurred images and the real-scene images. The experiment results demonstrate that the proposed image deblurring approach outperforms the existing methods for the images that are seriously blurred in direction motion.
- Źródło:
-
Optica Applicata; 2020, 50, 2; 185-198
0078-5466
1899-7015 - Pojawia się w:
- Optica Applicata
- Dostawca treści:
- Biblioteka Nauki
Artykuł