Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called ‘Russian Dolls’ ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition.

We propose a self-adaption focal length adjustment method of a compound and human hybrid eye with a non-uniform microlens array model (NUMLA) to reduce defocus aberration by using the liquid lens. The models are deduced and verified through simulations. The method can self-adaptively adjust the focal length according to object distance and image distance. The results show that (1) the RMS spot radii of the traditional uniform microlens array at different rings are 21 μm, 187 μm, 304 μm, 526 μm, and 803 μm. However, those of the NUMLA are 21 μm, 47 μm, 98 μm, 178 μm, and 287 μm, which indicates that the NUMLA can reduce the defocus aberration. (2) When the object distance and the image distance vary, the defocus aberration can be significantly reduced through the adjustment of the focal length, which validates the effectiveness of the proposed method. (3) The volumes of the liquid lens in the cavity at the peripheral rings are larger than that at the central rings. The results are beneficial for providing a simple solution to reduce the defocus aberration of the compound and human hybrid eye.

A compound eye and retina-like combination sensor based on a space-variant curved micro lens array (CMLA) is proposed to simultaneously offer the large FOV characteristic of a compound eye and retina-like feature of a single aperture eye. The mathematical models of the sensor are developed and the structure parameters of the space-variant CMLA are deduced. Modeling verification is carried out and the results show that the whole field of view (FOV) of the sensor is 105° and the optical information loss rate is 0.06 when the sector is 32. Imaging simulations illustrate that the sensor possesses the retina-like property, i.e., logarithmic-polar transformation. Meanwhile, the simulation results indicate that the overlapping angles between the two micro lenses on the adjacent rings can be reduced by decreasing the rings and the blind radius, and increasing the sectors. This work is beneficial for large FOV and time-efficient applications.

Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions.

To mitigate the conflict between imaging quality and speed, a spatially adaptive retina-like sampling method for 3-D imaging Lidar based on time-of-flight method is proposed. The differences between previous retina-like sampling method and the proposed method are described. Sampling points with dense distribution is for the area of interest while sparse distribution is for the area of uninterest, which obtains high imaging quality while consuming much less data acquisition time. Mathematical models of the spatially adaptive retina-like method are developed, and the key parameters are analyzed. To validate the spatially adaptive retina-like sampling method, we perform situational simulations to compare the proposed method with the previous one. Results demonstrate that the proposed method is capable of decreasing data acquisition time without considerable distortion of the interested target. Furthermore, the proposed method is analyzed under different scenes for single and multiple targets. Results illustrate that the proposed method performs better than the previous method.