Search

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km. [ABSTRACT FROM AUTHOR]

Single-pixel imaging (SPI) is a new type of imaging technology that uses a non-scanning single-pixel detector to image objects and has important application prospects and value in many fields. Most of the modulators currently used in SPI systems are digital micromirror device (DMD) modulators, which use a higher frequency for binary modulation than other alternatives. When modulating grayscale information, the modulation frequency is significantly reduced. This paper conducts research on multiple discrete objects in a scene and proposes using binary patterns to locate and image these objects. Compared with the existing methods of using gray patterns to locate and image multiple objects, the method proposed in this paper is more suitable for DMD-type SPI systems and has wider applicability and greater prospects. The principle of the proposed method is introduced, and the effectiveness of the method is experimentally verified. The experimental results show that, compared to traditional SPI methods, the number of patterns required by the proposed method is reduced by more than 85%. [ABSTRACT FROM AUTHOR]

Although high-resolution single-pixel imaging (SPI) can be achieved by under-sampling, low sampling ratio (SR) strategies often result in a loss of image details and lower image quality. Due to the limited memory of the spatial light modulator (SLM) and the time necessary to load such vast numbers of patterns, high-resolution SPI at high sampling rates has not yet been realized. In this work, we propose a one-dimensional single-pixel imaging (1D-SPI) technique to reconstruct high-quality images that can even surpass the full resolution of the SLM at 100% sampling ratio with extremely low data throughput. Only nearly one-thousandth as many modulation patterns as traditional 2D modulation methods are required, which significantly reduces the demand for massive memory capacity and the time to load these patterns. A 1540 × 1024 resolution SPI image is successfully demonstrated experimentally, indicating its tremendous promise in applications such as industrial inspection and satellite remote sensing. • A SPI method that can exceed resolution of spatial light modulators is proposed. • A High-resolution SPI is realized with very low data throughput. • High-quality SPI results are reconstructed at 100% sampling ratio. [ABSTRACT FROM AUTHOR]

• We propose a physical model-driven untrained deep convolutional autoencoder network for SPI and validate its performance from simulations and experiments. • We designed an end-to-end SPI reconstruction network, which can better reconstruct high-quality images from under-sampled measurements. • We perform a comparative study through the simulations and experiments. The results demonstrate that UCAN outperforms other existed SPI methods, including DGI, TVAL3, and GIDC. Single-pixel imaging (SPI) is a novel imaging modality which captures the images with a single-pixel detector by using a lot of time-varying modulation patterns. Nowadays, SPI reconstructions with data-driven deep learning had been verified for high-quality reconstructions under low sampling ratios. However, it faces a dilemma of hard-to-get sufficient training sets in many practical applications, e.g., long-range single-pixel imaging fields. Here, a model-driven SPI reconstruction method based on untrained convolutional autoencoder network (UCAN) is proposed. This framework does not need to pre-train on any dataset and can be automatically optimized, then eventually produce the restored images through the interplay between the neural network and the SPI physical model. Simulations confirm the superiorities of the proposed method over many other existed algorithms in the SPI field. Also, the reconstructions for long-range single-pixel imaging in real urban atmospheric environments demonstrate that our method has better denoising performance. We believe that the present work provides an alternative framework for SPI and paves the way for practical applications, e.g., long-range optical remote sensing and low-irradiative biological imaging. [ABSTRACT FROM AUTHOR]

Abstract Successive images of a scene are captured and then further processed to achieve the moving object tracking. However, due to modulation rate limitations of the spatial light modulator in single-pixel imaging (SPI) system, the imaging frame rate cannot meet the high-resolution and real-time requirements for object tracking. In this paper, we demonstrate a fast object tracking technique based on SPI with an ultra-low sampling rate that is independent of imaging. We construct modulation information that satisfies the projection conditions and can transform 2D images into 1D projection curves. The 1D projection curves, which provide the location information of the moving object, can be obtained with high resolution in real-time, and then the tracking of the moving object is realized. A background subtraction technique for tracking moving objects that removes static components from a scene is also proposed. The proposed technique is verified by computational simulations and laboratory experiments. In the laboratory experiments, we demonstrate that the proposed method can be used to track moving objects with less than 0.2% of the measurements established by the Nyquist criterion, and it presents 256 × 256 pixels at ∼ 177 fps. The reported technique accelerates the tracking speed of SPI and provides an efficient strategy for remote sensing and biomedical applications. [ABSTRACT FROM AUTHOR]

Image-free motion parameter acquisition has been continuously studied in recent years. This image-free method can effectively overcome the deficiencies of traditional imaging methods and improve the real-time and fast acquisition ability of motion parameters. However, existing methods only focus on the acquisition of motion position parameters. Herein, an image-free multi-motion parameter detection method is proposed for in-plane rotation of 2D object. A moment-modulated pattern was constructed to illuminate the moving object, and detected intensities were used to synchronously obtain the moving position and rotation angle. Numerical simulations and experiments verified the effectiveness of the proposed method, which provides a new idea for image-free multi-motion parameter acquisition. • An image-free multi-motion parameter detection method is proposed for in-plane rotation of 2D object. • Moment-modulated patterns were constructed to illuminate the moving object, and detected intensities were used to synchronously obtain the moving position and rotation angle. [ABSTRACT FROM AUTHOR]

Edge detection has been a fundamental component in many image segmentation and object detection systems. Recently, many researchers have implemented direct edge detection with the single-pixel imaging system. Compared with traditional edge detection methods, it does not need the procedures of acquiring the object image and then performing edge detection on the image. However, many previous works need to shift the primary illumination patterns at least twice to get the gradient information in multiple directions, which leads to the surge of the measurements. In this paper, we propose a novel single-pixel edge imaging scheme by obtaining the gradient Radon spectrum. Although the proposed scheme also needs to shift the primary illumination patterns once to obtain the gradient information of the object, compared with the Radon single-pixel imaging, no additional measurements are required. In addition, with the gradient Radon spectrum, this scheme can also directly obtain the positions of the straight edges which can be used for image correction, target positioning, and so on. The experimental and numerical results indicate that a high-quality edge image of the object can be restored by the proposed scheme, and the positions of the straight edges can be effectively located with the gradient Radon spectrum. • The edge image of the object can be obtained directly without acquiring the image in advance. • The positions of straight lines in the scene can be located with the gradient Radon spectrum. • Only two groups of patterns are used to get the object edge. • The proposed method can be used to image correction, image recognization, and so on. [ABSTRACT FROM AUTHOR]

The recently proposed Radon single pixel imaging (RSPI) is based on Radon transform and considered to be a prospective technique in many fields, such as computed tomography, feature detection, classification and so on. And because of the fact that the exact implementation of inverse discrete Radon transformation does not exist, an approximate algorithm, filtered back projection, is employed to reconstruct the result images in RSPI. However, the presence of disturbing streak artifacts emerging from high-contrast structures hinders the high-quality reconstructions, especially at low sampling rates. In order to get rid of this dilemma, a novel single pixel imaging technique, which employs a few projection angle patterns to achieve sparse projection angle sampling and utilizes compressive sensing to reconstruct the result images, is proposed. Simulation and experimental results demonstrate that the image quality reconstructed by the proposed technique is much higher than that of RSPI at low sampling rates. Additionally, the proposed technique also has advantages in a lot of fields such as tomographic reconstruction at low radiation. • Using compressed sensing to reconstruct images from sparse projection angle sampling. • Achieving high-quality reconstruction at a low sampling rate. • The proposed method has the potential to be applied to computed tomography. [ABSTRACT FROM AUTHOR]