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1. Deep compressed imaging via optimized pattern scanning

   https://doi.org/10.1364/PRJ.410556  

   Zhang, Kangning; Hu, Junjie; Yang, Weijian (March 2021, Photonics Research)

   The need for high-speed imaging in applications such as biomedicine, surveillance, and consumer electronics has called for new developments of imaging systems. While the industrial effort continuously pushes the advance of silicon focal plane array image sensors, imaging through a single-pixel detector has gained significant interest thanks to the development of computational algorithms. Here, we present a new imaging modality, deep compressed imaging via optimized-pattern scanning, which can significantly increase the acquisition speed for a single-detector-based imaging system. We project and scan an illumination pattern across the object and collect the sampling signal with a single-pixel detector. We develop an innovative end-to-end optimized auto-encoder, using a deep neural network and compressed sensing algorithm, to optimize the illumination pattern, which allows us to reconstruct faithfully the image from a small number of measurements, with a high frame rate. Compared with the conventional switching-mask-based single-pixel camera and point-scanning imaging systems, our method achieves a much higher imaging speed, while retaining a similar imaging quality. We experimentally validated this imaging modality in the settings of both continuous-wave illumination and pulsed light illumination and showed high-quality image reconstructions with a high compressed sampling rate. This new compressed sensing modality could be widely applied in different imaging systems, enabling new applications that require high imaging speeds. 

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2. Learnable-pattern scanning based deep compressed imaging

   https://doi.org/10.1117/12.2577998  

   Zhang, Kangning; Hu, Junjie; Yang, Weijian (March 2021, High-Speed Biomedical Imaging and Spectroscopy VI)

   Goda, Keisuke; Tsia, Kevin K. (Ed.)

   We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality. 

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3. Applications and assessment of an excitation-scanning hyperspectral imaging system

   https://doi.org/10.1117/12.2290120  

   Mayes, Sam A.; Leavesley, Silas J.; Rich, Tom C.; Klomkaew, Phiwat; Moore, Kaysie; Browning, Craig (February 2018, Proc. SPIE 10497, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVI)

   The majority of microscopic and endoscopic technologies utilize white light illumination. For a number of applications, hyper-spectral imaging can be shown to have significant improvements over standard white-light imaging techniques. This is true for both microscopy and in vivo imaging. However, hyperspectral imaging methods have suffered from slow application times. Often, minutes are required to gather a full imaging stack. Here we will describe and evaluate a novel excitation-scanning hyperspectral imaging system and discuss some applications. We have developed and are optimizing a novel approach called excitation-scanning hyperspectral imaging that provides an order of magnitude increased signal strength. This excitation scanning technique has enabled us to produce a microscopy system capable of high speed hyperspectral imaging with the potential for live video acquisition. The excitation-scanning hyperspectral imaging technology we developed may impact a range of applications. The current design uses digital strobing to illuminate at 16 wavelengths with millisecond image acquisition time. Analog intensity control enables a fully customizable excitation profile. A significant advantage of excitation-scanning hyperspectral imaging is can identify multiple targets simultaneously in real time. Finally, we are exploring utilizing this technology for a variety of applications ranging from measuring cAMP distribution in three dimensions within a cell to electrophysiology. 

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4. Imaging through scattering media via spatial–temporal encoded pattern illumination

   https://doi.org/10.1364/PRJ.456156  

   Zhao, Xingchen; Nie, Xiaoyu; Yi, Zhenhuan; Peng, Tao; Scully, Marlan O. (June 2022, Photonics Research)

   Optical imaging through scattering media has long been a challenge. Many approaches have been developed for focusing light or imaging objects through scattering media, but usually, they are either invasive, limited to stationary or slow-moving media, or require high-resolution cameras and complex algorithms to retrieve the images. By utilizing spatial–temporal encoded patterns (STEPs), we introduce a technique for the computation of imaging that overcomes these restrictions. With a single-pixel photodetector, we demonstrate non-invasive imaging through scattering media. This technique is insensitive to the motion of the media. Furthermore, we demonstrate that our image reconstruction algorithm is much more efficient than correlation-based algorithms for single-pixel imaging, which may allow fast imaging for applications with limited computing resources. 

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5. Blind-label subwavelength ultrasound imaging

   https://doi.org/10.1126/sciadv.ado2826  

   Lin, Jinuan; Ma, Chu (January 2025, Science Advances)

   There is a long-existing trade-off between the imaging resolution and penetration depth in acoustic imaging caused by the diffraction limit. Most existing approaches addressing this trade-off require controlled “labels,” i.e., metamaterials or contrast agents, to be deposited close to the objects and to either remain static or be tracked precisely during imaging. We propose a “blind-label” approach for acoustic subwavelength imaging. The blind labels are randomly distributed acoustic scatterers with deep-subwavelength sizes whose exact locations and trajectories are not necessary information in image reconstruction. The proposed method achieves the resolution of 0.24 wavelengths in ultrasound imaging experiments and 0.2 wavelengths in simulations, providing over 10 times improvement compared to the diffraction limit. We also elucidate the influence of scatterer size and concentration on imaging performance. The proposed “blind-label” approach relaxes the restrictions of existing acoustic subwavelength imaging technologies relying on controlled labels, therefore substantially improving the practicality of acoustic subwavelength imaging in biomedical ultrasound imaging, sonar, and nondestructive testing. 

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