| Autor: |
Qiang Wen, Yanqiu Liu, Ting Luo, Lele Chen, Jianhao Huang, Desen Song, Jingwen Jin |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Photonics Journal, Vol 14, Iss 3, Pp 1-9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2022.3176734 |
| Popis: |
In this paper, the image sensor compensation frameworks are established based on the correlation of pixel response characteristics; The pixel response model is established, and a method to measure the crosstalk of adjacent pixels of color image sensor under flat field light is proposed; at the same time, an artificial neural network training set is constructed by using the measured values and theoretical values of pixel response generated by combined exposures; a compensation method of using neural network compensation framework to replace high-dimensional neural network to traverse the image is proposed, which reduces the scale and training complexity of neural network; Finally, the corresponding spatial arrangement data of pixels are transformed into the frequency domain through the Fourier expansion algorithm, and the compensation effect is evaluated according to the change of high-frequency components. According to the experimental results, this method can effectively suppress color aliasing and crosstalk noise of the color image sensor. This paper provides a new method to compensate color aliasing and crosstalk noise. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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