Optimization method of Hadamard coding plate inγ‑ray computational ghost imaging

Owing to the constraints on the fabrication ofγ-ray coding plates with many pixels,few studies have been carried out onγ-ray computational ghost imaging.Thus,the development of coding plates with fewer pixels is essential to achieveγ-ray computational ghost imaging.Based on the regional similarity between Hadamard subcoding plates,this study presents an optimization method to reduce the number of pixels of Hadamard coding plates.First,a moving distance matrix was obtained to describe the regional similarity quantitatively.Second,based on the matrix,we used two ant colony optimization arrangement algorithms to maximize the reuse of pixels in the regional similarity area and obtain new compressed coding plates.With full sampling,these two algorithms improved the pixel utilization of the coding plate,and the compression ratio values were 54.2%and 58.9%,respectively.In addition,three undersampled sequences(the Harr,Russian dolls,and cake-cutting sequences)with different sampling rates were tested and discussed.With different sampling rates,our method reduced the number of pixels of all three sequences,especially for the Russian dolls and cake-cutting sequences.Therefore,our method can reduce the number of pixels,manufacturing cost,and difficulty of the coding plate,which is beneficial for the implementation and application ofγ-ray computational ghost imaging.

Computational ghost imaging with deep compressed sensing

Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.

Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.

Computational ghost imaging with compressed sensing based on a convolutional neural network

Computational ghost imaging(CGI)has recently been intensively studied as an indirect imaging technique.However,the image quality of CGI cannot meet the requirements of practical applications.Here,we propose a novel CGI scheme to significantly improve the imaging quality.In our scenario,the conventional CGI data processing algorithm is optimized to a new compressed sensing(CS)algorithm based on a convolutional neural network(CNN).CS is used to process the data collected by a conventional CGI device.Then,the processed data are trained by a CNN to reconstruct the image.The experimental results show that our scheme can produce higher quality images with the same sampling than conventional CGI.Moreover,detailed comparisons between the images reconstructed using the deep learning approach and with conventional CS show that our method outperforms the conventional approach and achieves a ghost image with higher image quality.

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

Feedback ghost imaging by gradually distinguishing and concentrating onto the edge area

Applications of ghost imaging are limited by the requirement on a large number of samplings. Based on the observation that the edge area contains more information thus requiring a larger number of samplings, we propose a feedback ghost imaging strategy to reduce the number of required samplings. The field of view is gradually concentrated onto the edge area,with the size of illumination speckles getting smaller. Experimentally, images of high quality and resolution are successfully reconstructed with much fewer samplings and linear algorithm.

Ghost imaging for online angiography

Ghost imaging(GI) has characteristics that make it promising for applications in life sciences and other fields, such as its high sensitivity and strong anti-interference compared with traditional imaging. This paper presents a new approach for online angiography using GI. Two signals are correlation-calculated to detect the object image: one of them is the random light field generated by a computer, and the other enters the optical fiber path after being transmitted to the detected object via a modulator. A new approach for the real-time imaging of intravascular flow, vascular wall structures, and components of atherosclerotic plaque is proposed, which has the advantages of a high sensitivity and anti-interference.