Fourier single-pixel imaging (FSI) has been proven to achieve excellent image quality when sampling all information in the Fourier domain. However, when the size of an imaging target is large, fully sampling Fourier coefficients would result in a large waste of sampling resources. An adaptive sampling method is proposed that is simple to implement and effectively improves the reconstructed image quality from undersampled Fourier coefficients. Through a rough estimation of spectrum energy distribution, the adaptive sampling trajectory is generated by the designed adaptive probability density function. Both the results of computational simulations and experiments demonstrate that the proposed method has overcome the limitations of the insufficient sampling rates, and the reconstruction images have obtained dramatic improvements. These improvements greatly promote the development of the FSI during undersampled conditions.
Correlated imaging is a research hotspot in recent years. It shows advantages over conventional optical imaging on scanning and imaging rate, noise immunity and so on, and has good application prospects in military electronic reconnaissance and other fields According to the basic principle of polarization correlation imaging, this paper established the spectral polarization BRDF model of rough surface. Taking two typical materials of aluminum alloy and PC plastic as target and background, the effects of wavelength on polarization correlated imaging of rough surface objects is analyzed. Theoretical analysis and simulation results show that the wavelength has little influence on the conventional correlated imaging, and the effects on the polarization correlated imaging appear in complex refractive index, linear polarization and contrast. The wavelength of the best imaging quality can be determined according to different material properties of the set target and the known background.
Compressive sensing ghost imaging (CSGI) is an imaging mechanism that can nonlocally obtain an unknown object’s information with a single-pixel detector by the correlation of intensity fluctuations. In the practical research and application of CSGI, object detection plays a crucial role in real-time monitoring and dynamic optimization of speckle pattern. We demonstrate, for the first time to our knowledge, how to solve the low-resolution and undersampling problems in CSGI object detection. The method we use is to combine generative adversarial networks (GANs) with object detection systems. The robustness of the object detection model can increase by generating reconstructed images of different resolutions and sampling rates for training. The experiment results have verified that the mean average precision of CSGI object detection using GANs has been improved 16.48% and 2.98% on MSCOCO 2017 compared with two traditional learning methods, respectively.
Ghost imaging is an imaging mechanism that can non-locally obtain an unknown object’s information with a single-pixel detector by the correlation of intensity fluctuations. To overcome the drawback of polarization compressive ghost imaging (PCGI), here we develop a novel adaptive polarization compressive ghost imaging (APCGI) method. By performing principal component analysis of the polarization statistics, we can compute the optimum unequal weighting coefficients forming as linear combinations of the light into bucket detectors. The specific steps of APCGI include calculating complete polarization parameters on the background, performing principal components analysis for optimal parameters, obtaining the information on the target-with-background scene. Experimental results demonstrate that adaptive polarization compressive ghost imaging performs better in restraining the background and pop out the details of targets as well as obtains better image quality.
Ghost imaging is an indirect system that allows the imaging of an object without directly seeing the object. The speckle pattern that contains the information about light and objects has increasingly become a popular topic in pseudothermal light ghost imaging. However, existing research still has encountered problems of poor imaging quality and slow sampling speeds. We propose a ghost imaging method based on N-order speckle patterns to recover the object (NSGI). The N-order speckle patterns combine N independent laser speckles individually produced by passing an expanded and collimated He–Ne laser through a digital micromirror device (DMD). The sampling frequency can be improved by controlling the trigger signals of different DMDs. The results of the simulation and experiment have verified that our method can increase sampling speed and reconstruction accuracy. In addition, NSGI can be applied to more applications by designing multiple independent speckles with different properties.
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