Contents
2024
Volume: 1 Issue 1
7 Article(s)
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Editorial
A new venue to publish and promote high-quality work in the field of imaging
Sylvain Gigan, and Xiaopeng Shao
Advanced Imaging
- Publication Date: Jul. 18, 2024
- Vol. 1, Issue 1, 010001 (2024)
Review Article
Future-proof imaging: computational imaging
Jinpeng Liu, Yi Feng, Yuzhi Wang, Juncheng Liu, Feiyan Zhou, Wenguang Xiang, Yuhan Zhang, Haodong Yang, Chang Cai, Fei Liu, and Xiaopeng Shao
Computational imaging overcomes traditional optical imaging limitations by incorporating encoding and decoding. It represents a paradigm shift in imaging technology, leveraging the manipulation and interpretation of the light field to extract richer information than that was previously attainable. This review explores Computational imaging overcomes traditional optical imaging limitations by incorporating encoding and decoding. It represents a paradigm shift in imaging technology, leveraging the manipulation and interpretation of the light field to extract richer information than that was previously attainable. This review explores the emergence and development history of computational imaging. By analyzing the essence of computational imaging from the perspective of the light field, this review categorizes the entire technological roadmap of the computational imaging field based on the imaging framework. This review can serve as a reference for researchers, producers, and policymakers on the main trends, frontiers, and future directions of computational imaging..
Advanced Imaging
- Publication Date: Jul. 17, 2024
- Vol. 1, Issue 1, 012001 (2024)
Research Article
Video-level and high-fidelity super-resolution SIM reconstruction enabled by deep learning
Hanchu Ye, Zitong Ye, Yunbo Chen, Jinfeng Zhang, Xu Liu, Cuifang Kuang, Youhua Chen, and Wenjie Liu
Structure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algoStructure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algorithm is prone to artifacts due to the high signal-to-noise ratio (SNR) requirement, and existing deep-learning SIM algorithms still have the potential to improve imaging speed. Here, we introduce a deep-learning-based video-level and high-fidelity super-resolution SIM reconstruction method, termed video-level deep-learning SIM (VDL-SIM), which has an imaging speed of up to 47 frame/s, providing a favorable observing experience for users. In addition, VDL-SIM can robustly reconstruct sample details under a low-light dose, which greatly reduces the damage to the sample during imaging. Compared with existing SIM algorithms, VDL-SIM has faster imaging speed than existing deep-learning algorithms, and higher imaging fidelity at low SNR, which is more obvious for traditional algorithms. These characteristics enable VDL-SIM to be a useful video-level super-resolution imaging alternative to conventional methods in challenging imaging conditions..
Advanced Imaging
- Publication Date: Apr. 05, 2024
- Vol. 1, Issue 1, 011001 (2024)
Feature-enhanced fiber bundle imaging based on light field acquisition
Haogong Feng, Runze Zhu, and Fei Xu
Optical fiber bundles frequently serve as crucial components in flexible miniature endoscopes, transmitting end-to-end images directly for medical and industrial applications. Each core usually acts as a single pixel, and the resolution of the image is limited by the core size and core spacing. We propose a method thatOptical fiber bundles frequently serve as crucial components in flexible miniature endoscopes, transmitting end-to-end images directly for medical and industrial applications. Each core usually acts as a single pixel, and the resolution of the image is limited by the core size and core spacing. We propose a method that exploits the hidden information embedded in the pattern within each core to break the limitation and obtain high-dimensional light field information and more features of the original image including edges, texture, and color. Intra-core patterns are mainly related to the spatial angle of captured light rays and the shape of the core. A convolutional neural network is used to accelerate the extraction of in-core features containing the light field information of the whole scene, achieve the transformation of in-core features to real details, and enhance invisible texture features and image colorization of fiber bundle images..
Advanced Imaging
- Publication Date: Apr. 09, 2024
- Vol. 1, Issue 1, 011002 (2024)
High-resolution 3D imaging through dense camouflage nets using single-photon LiDAR
Peng-Yu Jiang, Zheng-Ping Li, Wen-Long Ye, Ziheng Qiu, Da-Jian Cui, and Feihu Xu
The single-photon sensitivity and picosecond time resolution of single-photon light detection and ranging (LiDAR) can provide a full-waveform profile for retrieving the three-dimentional (3D) profile of the target separated from foreground clutter. This capability has made single-photon LiDAR a solution for imaging thrThe single-photon sensitivity and picosecond time resolution of single-photon light detection and ranging (LiDAR) can provide a full-waveform profile for retrieving the three-dimentional (3D) profile of the target separated from foreground clutter. This capability has made single-photon LiDAR a solution for imaging through obscurant, camouflage nets, and semitransparent materials. However, the obstructive presence of the clutter and limited pixel numbers of single-photon detector arrays still pose challenges in achieving high-quality imaging. Here, we demonstrate a single-photon array LiDAR system combined with tailored computational algorithms for high-resolution 3D imaging through camouflage nets. For static targets, we develop a 3D sub-voxel scanning approach along with a photon-efficient deconvolution algorithm. Using this approach, we demonstrate 3D imaging through camouflage nets with a
Advanced Imaging
- Publication Date: May. 15, 2024
- Vol. 1, Issue 1, 011003 (2024)
Label-free super-resolution stimulated Raman scattering imaging of biomedical specimens
Julien Guilbert, Awoke Negash, Simon Labouesse, Sylvain Gigan, Anne Sentenac, and Hilton B. de Aguiar
Far-field super-resolution microscopy has unraveled the molecular machinery of biological systems that tolerate fluorescence labeling. Conversely, stimulated Raman scattering (SRS) microscopy provides chemically selective high-speed imaging in a label-free manner by exploiting the intrinsic vibrational properties of spFar-field super-resolution microscopy has unraveled the molecular machinery of biological systems that tolerate fluorescence labeling. Conversely, stimulated Raman scattering (SRS) microscopy provides chemically selective high-speed imaging in a label-free manner by exploiting the intrinsic vibrational properties of specimens. Even though there were various proposals for enabling far-field super-resolution Raman microscopy, the demonstration of a technique compatible with imaging opaque biological specimens has been so far elusive. Here, we demonstrate a single-pixel-based scheme, combined with robust structured illumination, that enables super-resolution in SRS microscopy. The methodology is straightforward to implement and provides label-free super-resolution imaging of thick specimens, therefore paving the way for probing complex biological systems when exogenous labeling is challenging..
Advanced Imaging
- Publication Date: Jun. 11, 2024
- Vol. 1, Issue 1, 011004 (2024)
Snapshot macroscopic Fourier ptychography: far-field synthetic aperture imaging via illumination multiplexing and camera array acquisition | On the Cover
Sheng Li, Bowen Wang, Haitao Guan, Guoan Zheng, Qian Chen, and Chao Zuo
Fourier ptychography (FP) is an advanced computational imaging technique that offers high resolution and a large field of view for microscopy. By illuminating the sample at varied angles in a microscope setup, FP performs phase retrieval and synthetic aperture construction without the need for interferometry. ExtendingFourier ptychography (FP) is an advanced computational imaging technique that offers high resolution and a large field of view for microscopy. By illuminating the sample at varied angles in a microscope setup, FP performs phase retrieval and synthetic aperture construction without the need for interferometry. Extending its utility, FP’s principles can be adeptly applied to far-field scenarios, enabling super-resolution remote sensing through camera scanning. However, a critical prerequisite for successful FP reconstruction is the need for data redundancy in the Fourier domain, which necessitates dozens or hundreds of raw images to achieve a converged solution. Here, we introduce a macroscopic Fourier ptychographic imaging system with high temporal resolution, termed illumination-multiplexed snapshot synthetic aperture imaging (IMSS-SAI). In IMSS-SAI, we employ a
Advanced Imaging
- Publication Date: Jun. 06, 2024
- Vol. 1, Issue 1, 011005 (2024)
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