Open AccessTemperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems
Author(s) -
Chen Wei,
Qi Bingyun,
Liu Xu,
Li Haifeng,
Hao Xiang,
Peng Yifan
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200149
Subject(s) - robustness (evolution) , computer science , image quality , image sensor , lens (geology) , noise (video) , artificial intelligence , computer vision , electronic engineering , image (mathematics) , engineering , biochemistry , chemistry , gene , petroleum engineering
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 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.