Fast object imaging and classification based on circular harmonic Fourier moment detection.

Limited by the number of illumination fields and the speed of a spatial light modulator, single-pixel imaging (SPI) cannot realize real-time imaging and fast classification of an object. In this paper, we proposed the circular harmonic Fourier single-pixel imaging (CHF-SPI) for the first time to realize fast imaging and classification of objects. The light field distribution satisfies the circular harmonic Fourier formula, and the light intensity values of the single-pixel detector are equivalent to the circular harmonic Fourier moments. Then the target can be reconstructed under low sampling ratio by inverse transformation. Through simulation and experimental verification, clear imaging can be performed at a sampling ratio of 0.9%. In addition, circular harmonic Fourier moments are used to construct multi-distortion invariant to classify objects with rotation and scale change. The scale change multiples of objects can be calculated and the objects can be classified by using 10 light fields. It is of great significance to classify objects quickly without imaging.

Ghost imaging based on Fermat spiral laser array designed for remote sensing.

We propose a Fermat spiral laser array as illumination source in ghost imaging. Due to the aperiodic structure, the Fermat spiral laser array generates illuminating light field without spatial periodicity on the normalized second-order intensity correlation function. A single-pixel detector is used to receive the signal light from object for image reconstruction. The effects of laser array parameters on the quality of ghost imaging are analyzed comprehensively. Through experimental demonstration, the Fermat spiral laser array successfully achieves ghost imaging with high quality by combining with the compressive sensing reconstruction algorithm. This method is expected to be applied in remote sensing by combining with phased and collimated fiber laser array equipped with the high emitting power and high-speed modulation frequency.

Efficient and noise-resistant single-pixel imaging based on Pseudo-Zernike moments.

An efficient and noise-resistant single-pixel imaging (SPI) technique based on Pseudo-Zernike moments (PZ-SPI) is proposed. In this technique, the illumination light fields are modulated to satisfy the Pseudo-Zernike polynomials. Then the modulated light fields are projected onto the object. And the single-pixel detector is used to measure the reflected light intensities to calculate the Pseudo-Zernike moments. Finally, the object image is reconstructed by iterative summation of the product of the Pseudo-Zernike polynomials and the Pseudo-Zernike moments. Through the numerical simulation and experimental demonstration, PZ-SPI can effectively reconstruct image at low sampling ratios. Besides, comparing with the Fourier-SPI and Zernike-SPI, PZ-SPI has good robustness to background noise in SPI system. These advantages expand the application of PZ-SPI in complex environments.

Single-pixel imaging using discrete Zernike moments.

A novel single-pixel imaging (SPI) technique based on discrete orthogonal Zernike moments is proposed. In this technique, the target object is illuminated by two sets of Zernike basis patterns which satisfy the Zernike polynomials. The Zernike moments of object image are obtained by measuring the reflected light intensities through a single-pixel detector. And the object image is reconstructed by summing the product of Zernike polynomials and detected intensities iteratively. By theoretical and experimental demonstrations, an image with high quality is retrieved under compressive sampling. Moreover, the Zernike illuminating patterns are used for object classification due to the rotation invariant of Zernike moments. By measuring the amplitudes of a few specific Zernike moments through the SPI system, the rotated images with different angles and the same content are classified into the same class on experiment. This classification technique has the advantages of high efficiency and high accuracy due to the high modulation speed and high sensitivity of SPI system.

Efficient single-pixel imaging based on a compact fiber laser array and untrained neural network.

This paper presents an efficient scheme for single-pixel imaging (SPI) utilizing a phase-controlled fiber laser array and an untrained deep neural network. The fiber lasers are arranged in a compact hexagonal structure and coherently combined to generate illuminating light fields. Through the utilization of high-speed electro-optic modulators in each individual fiber laser module, the randomly modulated fiber laser array enables rapid speckle projection onto the object of interest. Furthermore, the untrained deep neural network is incorporated into the image reconstructing process to enhance the quality of the reconstructed images. Through simulations and experiments, we validate the feasibility of the proposed method and successfully achieve high-quality SPI utilizing the coherent fiber laser array at a sampling ratio of 1.6%. Given its potential for high emitting power and rapid modulation, the SPI scheme based on the fiber laser array holds promise for broad applications in remote sensing and other applicable fields.