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All-day thin-lens computational imaging with scene-specific learning recovery

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Abstract

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.

© 2022 Optica Publishing Group

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Supplementary Material (1)

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Supplement 1       Extra technical details and results

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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