Single-pixel imaging is an innovative imaging scheme and has received increasing attentions in recent years. It is applicable to imaging at non-visible wavelengths and imaging under low light conditions. However, single-pixel imaging has once encountered problems of low reconstruction quality and long data-acquisition time. This situation has been changed thanks to the developments of Hadamard single-pixel imaging (HSI) and Fourier single-pixel imaging (FSI). Both techniques are able to achieve high-quality and efficient imaging, remarkably improving the applicability of single-pixel imaging scheme. In this paper, we compare the performances of HSI and FSI with theoretical analysis and experiments. The results show that FSI is more efficient than HSI while HSI is more noise-robust than FSI. Our work may provide a guideline for researchers to choose suitable single-pixel imaging technique for their applications.

Hadamard single-pixel imaging versus Fourier single-pixel imaging

Single-pixel imaging can capture images using a detector without spatial resolution, which enables imaging in various situations that are challenging or impossible with conventional pixelated detectors. Here we report a compressive single-pixel imaging approach that can simultaneously encode and recover spatial, spectral, and 3D information of the object. In this approach, we modulate and condense the object information in the Fourier space and detect the light signals using a single-pixel detector. The data-compressing operation is similar to conventional compression algorithms that selectively store the largest coefficients of a transform domain. In our implementation, we selectively sample the largest Fourier coefficients, and no iterative optimization process is needed in the recovery process. We demonstrate an 88% compression ratio for producing a high-quality full-color 3D image. The reported approach provides a solution for information multiplexing in single-pixel imaging settings. It may also generate new insights for developing multi-modality computational imaging systems.

Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements

Single-pixel imaging which employs active illumination to acquire spatial information is an innovative imaging scheme and has received increasing attentions in recent years. It is applicable to imaging at non-visible wavelengths and imaging under low light conditions. However, single-pixel imaging has once encountered problems of low reconstruction quality and long data-acquisition time. Hadamard single-pixel imaging (HSI) and Fourier single-pixel imaging (FSI) are two representative deterministic model based techniques. Both techniques are able to achieve high-quality and efficient imaging, remarkably improving the applicability of single-pixel imaging scheme. In this paper, we compare the performances of HSI and FSI with theoretical analysis and experiments. The results show that FSI is more efficient than HSI while HSI is more noise-robust than FSI. Our work may provide a guideline for researchers to choose suitable single-pixel imaging technique for their applications.

Hadamard single-pixel imaging versus Fourier single-pixel imaging.

Single-pixel imaging via compressed sensing can reconstruct high-quality images from a few linear random measurements of an object known a priori to be sparse or compressive, by using a point/bucket detector without spatial resolution. Nevertheless, random measurements still have blindness, limiting the sampling ratios and leading to a harsh trade-off between the acquisition time and the spatial resolution. Here, we present a new compressive imaging approach by using a strategy we call cake-cutting, which can optimally reorder the deterministic Hadamard basis. The proposed method is capable of recovering images of large pixel-size with dramatically reduced sampling ratios, realizing super sub-Nyquist sampling and significantly decreasing the acquisition time. Furthermore, such kind of sorting strategy can be easily combined with the structured characteristic of the Hadamard matrix to accelerate the computational process and to simultaneously reduce the memory consumption of the matrix storage. With the help of differential modulation/measurement technology, we demonstrate this method with a single-photon single-pixel camera under the ulta-weak light condition and retrieve clear images through partially obscuring scenes. Thus, this method complements the present single-pixel imaging approaches and can be applied to many fields.

Super Sub-Nyquist Single-Pixel Imaging by Means of Cake-Cutting Hadamard Basis Sort.

We propose a method to perform color imaging with a single photodiode by using light structured illumination generated with a low-cost color LED array. The LED array is used to generate a sequence of color Hadamard patterns which are projected onto the object by a simple optical system while the photodiode records the light intensity. A field programmable gate array (FPGA) controls the LED panel allowing us to obtain high refresh rates up to 10 kHz. The system is extended to 3D imaging by simply adding a low number of photodiodes at different locations. The 3D shape of the object is obtained by using a non-calibrated photometric stereo technique. Experimental results are provided for an LED array with 32 × 32 elements.

Low-cost single-pixel 3D imaging by using an LED array

We propose and demonstrate a computational imaging technique that uses structured illumination based on a two-dimensional discrete cosine transform to perform imaging with a single-pixel detector. A scene is illuminated by a projector with two sets of orthogonal patterns, then by applying an inverse cosine transform to the spectra obtained from the single-pixel detector a full-colour image is retrieved. This technique can retrieve an image from sub-Nyquist measurements, and the background noise is easily cancelled to give excellent image quality. Moreover, the experimental set-up is very simple.

Coloured computational imaging with single-pixel detectors based on a 2D discrete cosine transform

An approach for improving ghost imaging (GI) quality is proposed. In this paper, an iteration model based on normalized GI is built through theoretical analysis. An adaptive threshold value is selected in the iteration model. The initial value of the iteration model is estimated as a step to remove the correlated noise. The simulation and experimental results reveal that the proposed strategy reconstructs a better image than traditional and normalized GI, without adding complexity. The NIDGI-AT scheme does not require prior information regarding the object, and can also choose the threshold adaptively. More importantly, the signal-to-noise ratio (SNR) of the reconstructed image is greatly improved. Therefore, this methodology represents another step towards practical real-world applications.

Normalized iterative denoising ghost imaging based on the adaptive threshold

In this paper, an evaluation criterion based on image complexity is proposed in ghost imaging. According to the iterative performance of ghost imaging, characteristic factors of describing image complexity are introduced for seeking a new evaluation criterion to improve evaluation method. The proposed image complexity can be utilized to assess the iterative performance of different ghost imaging algorithms. The assessment results indicate that the proposed image complexity has a similar function with SNR, which is used to evaluate the iterative performance of ghost imaging. Compared with other existing evaluation methods, the obvious advantage is availability when the original image is unknown, and experiment results demonstrate that the new evaluation criterion is valid in ghost imaging.

An evaluation criterion based on image complexity for ghost imaging

Ghost imaging (GI) technology has attracted increasing attention as a new imaging technique in recent years. However, the signal-to-noise ratio (SNR) of GI with pseudo-thermal light needs to be improved before it meets engineering application demands. We therefore propose a new scheme called positive–negative correspondence normalized GI based on an adaptive threshold (PCNGI-AT) to achieve a good performance with less amount of data. In this work, we use both the advantages of normalized GI (NGI) and positive–negative correspondence GI (P–NCGI). The correctness and feasibility of the scheme were proved in theory before we designed an adaptive threshold selection method, in which the parameter of object signal selection conditions is replaced by the normalizing value. The simulation and experimental results reveal that the SNR of the proposed scheme is better than that of time-correspondence differential GI (TCDGI), avoiding the calculation of the matrix of correlation and reducing the amount of data used. The method proposed will make GI far more practical in engineering applications.

https://iopscience.iop.org/article/10.1088/1612-2011/13/11/115202/pdf

Positive-negative corresponding normalized ghost imaging based on an adaptive threshold

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Coloured computational imaging with single-pixel detectors based on a 2D discrete cosine transform

Normalized iterative denoising ghost imaging based on the adaptive threshold

An evaluation criterion based on image complexity for ghost imaging

Positive-negative corresponding normalized ghost imaging based on an adaptive threshold