The fundamental trade-off between spatial resolution and imaging distance poses a significant challenge for current imaging techniques,such as those used in modern biomedical diagnosis and remote sensing.Here,we introduce a new conceptual method for imaging dynamic amplitude-phase-mixed objects,termed relay-projection microscopic telescopy(rPMT),which fundamentally challenges conventional light collection techniques by employing non-line-of-sight light collection through square-law relay-projection mechanisms.We successfully resolved tiny features measuring 2.76 μm,22.10 μm,and 35.08 μm for objects positioned at distances of 1019.0 mm,26.4 m,and 96.0 m,respectively,from single-shot spatial power spectrum images captured on the relay screen;these results demonstrate that the resolution capabilities of rPMT significantly surpass the Abbe diffraction limit of the 25 mm-aperture camera lens at the respective distances,achieving resolution improvement factors of 7.9,25.4,and 58.2.The rPMT exhibits long-distance,wide-range,high-resolution imaging capabilities that exceed the diffraction limit of the camera lens and the focusing range limit,even when the objects are obscured by a scattering medium.The rPMT enables telescopic imaging from centimeters to beyond hundreds of meters with micrometer-scale resolution using simple devices,including a laser diode,a portable camera,and a diffusely reflecting whiteboard.Unlike contemporary high-resolution imaging techniques,our method does not require labeling reagents,wavefront modulation,synthetic receive aperture,or ptychography scanning,which significantly reduce the complexity of the imaging system and enhance the application practicality.This method holds particular promise for in-vivo label-free dynamic biomedical microscopic imaging diagnosis and remote surveillance of small objects.

For speckle-correlation-based scattering imaging,an iris is generally used next to the diffuser to magnify the speckle size and enhance the speckle contrast,which limits the light flux and makes the setup cooperative.Here,we experimentally demonstrate a non-iris speckle-correlation imaging method associated with an image resizing process.The experimental results demonstrate that,by estimating an appropriate resizing factor,our method can achieve high-fidelity noncooperative speckle-correlation imaging by digital resizing of the raw captions or on-chip pixel binning without iris.The method opens a new door for noncooperative high-frame-rate speckle-correlation imaging and benefits scattering imaging for dynamic objects hidden behind opaque barriers.

Compressive hyperspectral imaging (CHI) with random encoding mask usually suffers from various noises and artifacts. Inspired by the dual-camera CHI techniques based on hyperspectral (HS) and multispectral (MS) image fusion, herein, we present a single-camera push-broom CHI method based on self-fusion refinement (SFR). In this work, the MS guidance image for data fusion is derived directly from the raw solved HS data cube itself rather than any additional data source, which turns cross-fusion into self-fusion; furthermore, a modified joint bilateral filtering (JBF) fusion algorithm is developed to adapt this self-fusion problem, and an adaptive range Gaussian radius is adopted to avoid the invalidation or over-smoothing effects so as to ensure spatial and spectral improvement. The visualized and quantitative assessment results both demonstrate that the proposed method achieves high-quality HS imaging in terms of noise and artifact removal and spatial–spectral fidelity. Furthermore, the proposed method has a great flexibility and extensibility, whose performances highly depend on the exact fusion algorithm adopted, and a more suitable fusion algorithm will lead to better reconstruction quality; herein, the SFR process by the modified JBF achieves better performances than SFR by guided filtering (GF) or Markov random field (MRF).

The design and calibration of the dispersive device in a hyperspectral imager significantly affect the performance of hyperspectral imaging, especially the spectral accuracy. To achieve high-accuracy hyperspectral imaging over the visible band, firstly, the geometric and dispersive parameters of the double Amici prism (DAP) that serves as a dispersive device in the direct-vision push-broom compressive hyperspectral imager (PBCHI) are designed and optimized; secondly, a calibration method based on the numerical calculation of the DAP model is put forward, which can turn the conventional pixel-wise dispersive shift calibration by a monochromator into a group of numerical calculations; lastly, a PBCHI prototype is built to test the performances of the designed and calibrated DAP and the hyperspectral imager. The calibration experiments demonstrate that the mean squared error (MSE) of the dispersive pixel shifts calibrated by the proposed numerical method is 0.1774, which indicates the calibration result of the proposed method is consistent with the directly calibrated result. Furthermore, after this numerical calculation, the spectral signatures of the reconstructed cubes of the DAP-based PBCHI system show consistency with the ground truth. This work will benefit the design and calibration of the DAP-based hyperspectral imager.

Imaging objects hidden behind opaque layers is significant in many fields, with applications ranging from biomedical imaging to defense security. Techniques based on memory-effect scattering imaging have been developed in the past decade. The existing memory-effect-based scattering imaging techniques can be divided into two categories based on the working principle of light sources. In these methods, phase-retrieval algorithm is used to reconstruct object from the power spectrum diffraction patterns as the last step. Although both of them achieve single-shot scattering imaging, the experimental set-up is quite different. It is noted that the coherent diffraction imaging is introduced to the scattering imaging field using the visible coherent light. The principle and setup of the aforementioned two methods are analyzed and summarized respectively. We experimentally demonstrate the reconstruction and evaluate the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Image Measurement (SSIM). As these technologies are limited to short range and memory effect range, the potential to imaging with wide field of view and long distance requires further exploration.

Compressive hyperspectral images often suffer from various noises and artifacts, which severely degrade the imaging quality and limit subsequent applications. In this paper, we present a refinement method for compressive hyperspectral data cubes based on self-fusion of the raw data cubes, which can effectively reduce various noises and improve the spatial and spectral details of the data cubes. To verify the universality, flexibility, and extensibility of the self-fusion refinement (SFR) method, a series of specific simulations and practical experiments were conducted, and SFR processing was performed through different fusion algorithms. The visual and quantitative assessments of the results demonstrate that, in terms of noise reduction and spatial-spectral detail restoration, the SFR method generally is much better than other typical denoising methods for hyperspectral data cubes. The results also indicate that the denoising effects of SFR greatly depend on the fusion algorithm used, and SFR implemented by joint bilateral filtering (JBF) performs better than SRF by guided filtering (GF) or a Markov random field (MRF). The proposed SFR method can significantly improve the quality of a compressive hyperspectral data cube in terms of noise reduction, artifact removal, and spatial and spectral detail improvement, which will further benefit subsequent hyperspectral applications.