Double-layer polarization-independent achromatic metasurface array for optical fiber bundle coupling in microendoscope.

Optical fiber bundle-based microendoscope, which is significant in clinical diagnosis and industrial detection, calls for miniaturization of the probe and high-resolution observation. Here, we propose a double-layer metasurface array borrowing the structures of insect compound eyes to meet both requirements instead of traditional optical components. Each unit in the array aims for an incident field of view, focusing light at the center of the fiber end face with no chromatic aberration at the wavelengths of 470 nm, 530 nm and 630 nm. The metasurface array is composed of a series of isotropic TiO2 nanopillars which are special selected after considering resonance mode and angular dispersion characteristics, etched on both sides of a silica substrate, with the individual functions of deflecting and focusing. In image space, numerical aperture (NA) is 0.287 and the particular layout of two layers achieve zero telecentricity theoretically, which meet the requirements of optical fiber bundle coupling. A unit for incident angle of 20° is shown to validate our design approach numerically, which obtains a focused spot close to the diffraction limit. The compact and ultrathin metasurface could greatly reduce the size of the probe in optical fiber bundle based microendoscope while ensuring the imaging quality.

Residual image recovery method based on the dual-camera design of a compressive hyperspectral imaging system.

Compressive hyperspectral imaging technology can quickly detect the encoded two-dimensional measurements and reconstruct the three-dimensional hyperspectral images offline, which is of great significance for object detection and analysis. To provide more information for reconstruction and improve the reconstruction quality, some of the latest compressive hyperspectral imaging systems adopt a dual-camera design. To utilize the information from additional camera more efficiently, this paper proposes a residual image recovery method. The proposed method takes advantage of the structural similarity between the image captured by the additional camera and the hyperspectral image, combining the measurements from the additional camera and coded aperture snapshot spectral imaging (CASSI) sensor to construct an estimated hyperspectral image. Then, the component of the estimated hyperspectral image is subtracted from the measurement of the CASSI sensor to obtain the residual data. The residual data is used to reconstruct the residual hyperspectral image. Finally, the reconstructed hyperspectral image is the sum of the estimated and residual image. Compared with some state-of-the-art algorithms based on such systems, the proposed method can significantly improve the reconstruction quality of hyperspectral image.

Compressive single-pixel hyperspectral imaging using RGB sensors.

Hyperspectral imaging that obtains the spatial-spectral information of a scene has been extensively applied in various fields but usually requires a complex and costly system. A single-pixel detector based hyperspectral system mitigates the complexity problem but simultaneously brings new difficulties on the spectral dispersion device. In this work, we propose a low-cost compressive single-pixel hyperspectral imaging system with RGB sensors. Based on the structured illumination single-pixel imaging configuration, the lens-free system directly captures data by the RGB sensors without dispersion in the spectral dimension. The reconstruction is performed with a pre-trained spatial-spectral dictionary, and the hyperspectral images are obtained through compressive sensing. In addition, the spatial patterns for the structured illumination and the dictionary for the sparse representation are optimized by coherence minimization, which further improve the reconstruction quality. In both spatial and spectral dimensions, the intrinsic sparse properties of the hyperspectral images are made full use of for high sampling efficiency and low reconstruction cost. This work may introduce opportunities for optimization of computational imaging systems and reconstruction algorithms towards high speed, high resolution, and low cost future.