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Register a new userImprovements of computational ghost imaging by using Special-Hadamard patterns
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Added by
Jie Hou
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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We introduced a new kind of patterns named Special-Hadamard patterns, which could be used as structured illuminations of computational ghost imaging. Special-Hadamard patterns can get a better image quality than Hadamard patterns in a noisy environment. We can completely reconstruct the original object in a noiseless environment by using Special-Hadamard patterns, and the size of object also can be adjusted arbitrarily, these advantages cannot be achieved by other common patterns. We also performed simulations to compare the results of Special Hadamard patterns with the results of Hadamard patterns. We found Special Hadamard patterns can greatly improve the image quality of computational ghost imaging.
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Computational ghost imaging is a promising technique for single-pixel imaging because it is robust to disturbance and can be operated over broad wavelength bands, unlike common cameras. However, one disadvantage of this method is that it has a long calculation time for image reconstruction. In this paper, we have designed a dedicated calculation circuit that accelerated the process of computational ghost imaging. We implemented this circuit by using a field-programmable gate array, which reduced the calculation time for the circuit compared to a CPU. The dedicated circuit reconstructs images at a frame rate of 300 Hz.
Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three- dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.
Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.
In this paper, we propose an advanced framework of ghost edge imaging, named compressed ghost edge imaging (CGEI). In the scheme, a set of structured speckle patterns with pixel shifting are illuminated on an unknown object, and the output is collected by a bucket detector without any spatial resolution. By using compressed sensing algorithm, we obtain the horizontal and vertical edge information of the unknown object with the bucket detector detection results and the known structured speckle patterns. The edge is finally constructed by the two-dimentional edge information. The experimental and numerical simulations results show that the proposed scheme has a higher quality and reduces the number of measurements, in comparison with the existed edge detection schemes based on ghost imaging.
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