Multi-resolution progressive computational ghost imaging

Zhou, Cheng; Tian, Tian; Gao, Chao; Gong, Weiping; Song, Lijun

doi:10.1088/2040-8986/ab1471

Cited by 35 publications

(23 citation statements)

References 29 publications

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“…Here, we explore theoretically this multiscale property and provide a useful formulation for its e cient application for CS (Supplement A). This provides us with a method to choose directly the necessary multiscale patterns, as opposed to previous methods 8, 9 that found the patterns manually by comparing the patterns of the high-resolution Hadamard matrix with the lower one.…”

Section: The Multiscale Sensing Matrixmentioning

confidence: 99%

Progressive Compressive Sensing of Large Images with Multiscale Deep Learning Reconstruction

Kravets

Stern

2021

Preprint

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Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last fifteen years. Yet, its application for large and high-resolution imaging remains challenging in terms of the computation and acquisition effort involved. Often, low-resolution imaging is sufficient for most of the considered tasks and only a fraction of cases demand high resolution, but the problem is that the user does not know in advance when high-resolution acquisition is required. To address this, we propose a multiscale progressive CS method for the high-resolution imaging. The progressive sampling refines the resolution of the image, while incorporating the already sampled low-resolution information, making the process highly efficient. Moreover, the multiscale property of the progressively sensed samples is capitalized for a fast, deep learning (DL) reconstruction, otherwise infeasible due to practical limitations of training on high-resolution images. The progressive CS and the multiscale reconstruction method are analyzed numerically and demonstrated experimentally with a single pixel camera imaging system. We demonstrate 4-megapixel size progressive compressive imaging with about half the overall number of samples, more than an order of magnitude faster reconstruction, and improved reconstruction quality compared to alternative conventional CS approaches.

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Section: The Multiscale Sensing Matrixmentioning

confidence: 99%

Progressive Compressive Sensing of Large Images with Multiscale Deep Learning Reconstruction

Kravets

Stern

2021

Preprint

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“…For example, SPC usually satisfies the process of bucket detection in long-range imaging, and for CGI the energy of the source is limited by the damage threshold of the modulator so that the detection range is restricted [25,26]. Recently, some works on the influence of the source's energy fluctuation and the background light to CGI have been reported [27,28,29,30,31], but there is no investigation on the performance comparison between SPC and CGI. In this paper, we study the influence of some different types of light disturbance to both CGI and SPC, and the method to further enhance the quality of SPC is also proposed.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of computational ghost imaging versus single-pixel camera in light disturbance environment

Gong

2021

Preprint

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Computational ghost imaging (CGI) and single-pixel camera (SPC) are two types of single-pixel imaging and attracts increasing interest in recent years. The performance differences of CGI and SPC in difference light disturbance environment are investigated. In comparison with CGI, we demonstrate that the quality of SPC is always better in the same conditions and local light disturbance has a larger influence to CGI/SPC than global light disturbance. In addition, a method to improve the reconstruction result of SPC is proposed if the source's energy is unstable and there is light disturbance in the detection process, and its validity is also verified by numerical simulation.

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“…Obviously, binary DOP is the key to improve imaging speed in CGI based on DMD, which happens to be satisfied by Hadamard basics pattern. Therefore, to further promote the practical application, the optimization coding of Hadamard basics pattern for CGI was considered [22,[33][34][35][36]. Sun et al proposed a Russian Dolls ordering of the Hadamard basis, which uses less computational resources to achieve the imaging quality of conventional methods [33].…”

Section: Introductionmentioning

confidence: 99%

“…Sun et al proposed a Russian Dolls ordering of the Hadamard basis, which uses less computational resources to achieve the imaging quality of conventional methods [33]. Then, a multi-resolution CGI method using optimized Hadamard basis was reported by Zhou et al [34]. Subsequently, Yu et al proposed origami and cakecutting Hadamard basis sort to realize sub-Nyquist imaging [35].…”

Section: Introductionmentioning

confidence: 99%

Gold Matrix Ghost Imaging

Zhao,

Wang,

Zhang

et al. 2019

Preprint

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Light field modulation matrix is closely related to the quality of reconstructed image in ghost imaging. The orthogonality light field modulation matrix with better noise immunity and high quality reconstructed image is urgently needed in the practical application of ghost imaging.In this work, we propose a Gold matrix ghost imaging method with excellent imaging and antinoise performance, which is proud of the pseudo-randomness and orthogonality of the deterministic Gold matrix. Numerical simulation and experimental results show that the Gold matrix has the characteristics of both random speckle and Hadamard matrix, and the reconstructed images are superior to the two modulation matrixes under the condition of noise, which shows better imaging robustness.

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Multi-resolution progressive computational ghost imaging

Cited by 35 publications

References 29 publications

Progressive Compressive Sensing of Large Images with Multiscale Deep Learning Reconstruction

Progressive Compressive Sensing of Large Images with Multiscale Deep Learning Reconstruction

Performance comparison of computational ghost imaging versus single-pixel camera in light disturbance environment

Gold Matrix Ghost Imaging

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