Modern imaging optics ensures high-quality photography at the cost of a complex optical form factor that deviates from the portability. The drastic development of image processing algorithms, especially advanced neural networks, shows great promise to use thin optics but still faces the challenges of residual artifacts and chromatic aberration. In this work, we investigate photorealistic thin-lens imaging that paves the way to actual applications by exploring several fine-tunes. Notably, to meet all-day photography demands, we develop a scene-specific generative-adversarial-network-based learning strategy and develop an integral automatic acquisition and processing pipeline. Color fringe artifacts are reduced by implementing a chromatic aberration pre-correction trick. Our method outperforms existing thin-lens imaging work with better visual perception and excels in both normal-light and low-light scenarios.

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.