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Published April 2023 | public
Journal Article

Learning-based super-resolution interpolation for sub-Nyquist sampled laser speckles

Abstract

Information retrieval from visually random optical speckle patterns is desired in many scenarios yet considered challenging. It requires accurate understanding or mapping of the multiple scattering process, or reliable capability to reverse or compensate for the scattering-induced phase distortions. In whatever situation, effective resolving and digitization of speckle patterns are necessary. Nevertheless, on some occasions, to increase the acquisition speed and/or signal-to-noise ratio (SNR), speckles captured by cameras are inevitably sampled in the sub-Nyquist domain via pixel binning (one camera pixel contains multiple speckle grains) due to finite size or limited bandwidth of photosensors. Such a down-sampling process is irreversible; it undermines the fine structures of speckle grains and hence the encoded information, preventing successful information extraction. To retrace the lost information, super-resolution interpolation for such sub-Nyquist sampled speckles is needed. In this work, a deep neural network, namely SpkSRNet, is proposed to effectively up sample speckles that are sampled below 1/10 of the Nyquist criterion to well-resolved ones that not only resemble the comprehensive morphology of original speckles (decompose multiple speckle grains from one camera pixel) but also recover the lost complex information (human face in this study) with high fidelity under normal- and low-light conditions, which is impossible with classic interpolation methods. These successful speckle super-resolution interpolation demonstrations are essentially enabled by the strong implicit correlation among speckle grains, which is non-quantifiable but could be discovered by the well-trained network. With further engineering, the proposed learning platform may benefit many scenarios that are physically inaccessible, enabling fast acquisition of speckles with sufficient SNR and opening up new avenues for seeing big and seeing clearly simultaneously in complex scenarios.

Additional Information

© 2023 Chinese Laser Press. The authors would like to thank the Photonics Research Institute and University Research Facility in Big Data Analytics of the Hong Kong Polytechnic University for facility and technical support. Author Contributions. H. L., Z. Y, and Q. Z. contributed equally to this work. Y. L., H. L., Z. Y, and Y. L. worked on simulation. H. L. and Z. Y. contributed to the experiments. C. W. and H. L helped to fulfill the low-light experiments. L. V. W., Y. Z., and P. L. conceived and supervised the research. All authors contributed to results discussion, manuscript writing, and revision. Funding. Agency for Science, Technology and Research (A18A7b0058); Innovation and Technology Commission (GHP/043/19SZ, GHP/044/19GD); Hong Kong Research Grant Council (15217721, C5078-21EF, R5029-19); Guangdong Science and Technology Department (2019A1515011374, 2019BT02X105); National Natural Science Foundation of China (81627805, 81930048). Data Availability. 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. The authors declare no conflicts of interest.

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023