Name
Abstract | ||
|---|---|---|
Imaging systems are widely applied in harsh environments where the performance of shallow-designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0-80 degrees C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/aisy.202200149 | ADVANCED INTELLIGENT SYSTEMS |
Keywords | DocType | Volume |
computational imaging, deep learning, generative adversarial networks (GANs), multibranch models, temperature-robust imaging | Journal | 4 |
Issue | Citations | PageRank |
10 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Chen | 1 | 49 | 19.09 |
| Bingyun Qi | 2 | 0 | 0.34 |
| X.L. Liu | 3 | 11 | 11.83 |
| Haifeng Li | 4 | 1 | 1.37 |
| Xiang Hao | 5 | 0 | 0.68 |
| Yifan Peng | 6 | 41 | 7.51 |