Article
Physics, Multidisciplinary
Cheng Zhang, Jiaxuan Zhou, Feng Wu, Sui Wei
Summary: A compressive holography approach with autofocusing from a single-shot subsampled hologram is proposed in this paper. The approach combines a subsampling mechanism to establish a subsampled compressive holographic imaging model and an eigenvalues-based autofocusing algorithm to achieve autofocus reconstruction under subsampling condition in compressive holography. Furthermore, a multi-scale search algorithm is proposed to improve the accuracy of autofocus. Numerical experiments demonstrate the feasibility of the proposed approach in terms of precisely autofocusing and twin-image-free reconstruction from single-shot subsampled holograms.
Article
Computer Science, Information Systems
Xiuli Chai, Haiyang Wu, Zhihua Gan, Daojun Han, Yushu Zhang, Yiran Chen
Summary: A novel double color image encryption algorithm combining 2D compressive sensing with an embedding technique is proposed in this paper, achieving compression and encryption simultaneously and obtaining visually meaningful cipher images. Confusion of the compressed cipher images is done by index sequences generated from a 6D hyperchaotic system, while the relationship of the algorithm with plain images is enhanced by embedding feature parameters into the carrier image.
INFORMATION SCIENCES
(2021)
Article
Engineering, Mechanical
Guan-Sen Dong, Hua-Ping Wan, Yaozhi Luo, Michael D. Todd
Summary: Vibration data often contains important information about dynamic characteristics, but the large volume of data from high-frequency vibration poses challenges in transmission and storage. We propose a novel deep learning method using DCGAN for vibration data reconstruction, which directly learns the mapping between compressed and original signals and achieves high computational efficiency and accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Ejaz Ul Haq, Huang Jianjun, Xu Huarong, Kang Li
Summary: A new multi-rate method utilizing deep neural networks for block-based compressive sensing is proposed, capable of smartly allocating sampling rates and effectively removing blocking artifacts.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Chemistry, Analytical
Assia El Mahdaoui, Abdeldjalil Ouahabi, Mohamed Said Moulay
Summary: This paper presents a compressed sensing reconstruction method called DCSR, which combines total variation regularization and non-local self-similarity constraint. Compared to other methods, DCSR achieves significant improvements in denoising efficiency and visual quality.
Article
Engineering, Electrical & Electronic
Lihao Zhuang, Liquan Shen, Zhengyong Wang, Yinyi Li
Summary: This paper proposes a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. The framework consists of three sub-networks: underwater priors extraction and guidance network, sampling matrix generation network, and channel-wise reconstruction network. Experimental results demonstrate that our framework outperforms other state-of-the-art methods in terms of underwater image reconstruction quality.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Mathematics
Zizhao Xie, Jingru Sun, Yiping Tang, Xin Tang, Oluyomi Simpson, Yichuang Sun
Summary: This paper proposes a merging compression and encryption chaos image visual encryption scheme. A dictionary matrix D is constructed with the plain image by the K-SVD algorithm, which can encrypt the image while sparsing. An improved Zeraoulia-Sprott chaotic map and logistic map are employed to generate three S-Boxes, which are used for scrambling, diffusion, and embedding operations. The secret keys of this scheme contain the initial value of the chaotic system and the dictionary matrix D, significantly increasing the key space, plain image correlation, and system security. Simulation results show that the proposed scheme can resist most attacks, has a larger key space, higher plain image correlation, and better image restoration quality compared to existing schemes, improving image encryption processing efficiency and security.
Article
Computer Science, Artificial Intelligence
Sidi Lu, Xin Yuan, Aggelos K. Katsaggelos, Weisong Shi
Summary: In this work, reinforcement learning is applied to video compressive sensing to adapt the compression ratio. The gap in previous studies of how to adapt B in the video SCI system is filled using RL. An RL model and various convolutional neural networks are employed to achieve adaptive sensing of video SCI systems. Additionally, the performance of an object detection network is utilized for RL-based adaptive video compressive sensing. This proposed adaptive SCI method can be implemented in low cost and real time, and takes the technology one step further towards real applications of video SCI.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Optics
Xiaoyu Jin, Jie Zhao, Dayong Wang, Lu Rong, Yunxin Wang, John J. Healy, Shufeng Lin
Summary: This paper proposes an iterative denoising phase retrieval method for solving the twin-image problem in continuous-wave terahertz in-line digital holography. The method improves the quality of reconstructed images by adding the positive absorption constraint and using a new image denoising algorithm. The validity of this approach is demonstrated by high-quality imaging of samples and its robustness to noise in the hologram.
OPTICS AND LASERS IN ENGINEERING
(2022)
Article
Optics
Piotr Arcab, Mikolaj Rogalski, Maciej Trusiak
Summary: Lensless digital holographic microscopy enables low-cost compact imaging with enhanced precision by eliminating twin-image errors.
OPTICS AND LASERS IN ENGINEERING
(2024)
Article
Mathematics, Interdisciplinary Applications
Hua Ren, Shaozhang Niu, Jiajun Chen, Ming Li, Zhen Yue
Summary: This paper introduces a novel encryption system based on compressive sensing and vector quantization. By compressing sparser error matrices in the secret generation phase, this method improves compression performance and the quality of decrypted images. The use of a smooth function in the embedding phase to find underlying relationships and determine modifiable values further enhances the security of the system.
FRACTAL AND FRACTIONAL
(2022)
Article
Computer Science, Artificial Intelligence
Jhony Luiz de Almeida, Eros Comunello, Antonio Sobieranski, Anita da Maria Rocha Fernandes, Gabriel Schade Cardoso
Summary: Digital holography is a process that recreates three-dimensional representations of objects, with the in-line configuration setup providing higher resolution imaging but facing the twin-image problem. This paper presents a numerical approach to consistently suppress the twin-image problem in in-line holography through image subtraction and edge detection techniques.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Chemistry, Analytical
Yue Xiao, Lei Yuan, Junyu Wang, Wenxin Hu, Ruimin Sun
Summary: A sound field reconstruction method based on Bayesian compressive sensing is proposed to solve the problem of sound field reconstruction with fewer measurement points. The method establishes a sound field reconstruction model based on a combination of the equivalent source method and sparse Bayesian compressive sensing. The MacKay iteration of the relevant vector machine is used to infer the hyperparameters and estimate the maximum a posteriori probability of both the sound source strength and noise variance. The proposed method achieves sparse reconstruction of the sound field by determining the optimal solution for sparse coefficients with an equivalent sound source. Numerical simulation results show that the proposed method has higher accuracy over the entire frequency range compared to the equivalent source method, indicating better reconstruction performance and wider frequency applicability with undersampling. Moreover, in environments with low signal-to-noise ratios, the proposed method exhibits significantly lower reconstruction errors than the equivalent source method, indicating superior anti-noise performance and greater robustness in sound field reconstruction. The experimental results further verify the superiority and reliability of the proposed method for sound field reconstruction with limited measurement points.
Article
Radiology, Nuclear Medicine & Medical Imaging
Andreas Steven Kunz, Jonas Schmalzl, Henner Huflage, Karsten Sebastian Luetkens, Theresa Sophie Patzer, Philipp Josef Kuhl, Philipp Gruschwitz, Bernhard Petritsch, Rainer Schmitt, Thorsten Alexander Bley, Jan -Peter Grunz
Summary: The study compared the diagnostic performance of gantry-free cone-beam CT (CBCT) with two-dimensional radiography in acute elbow trauma. The results showed that CBCT had higher sensitivity in detecting fractures, articular involvement, and multi-fragmentary patterns compared to radiography. The use of CBCT data also significantly improved the diagnostic confidence of the readers.
Article
Computer Science, Artificial Intelligence
Dongming Huo, Yueyou Qiu, Chao Han, Lisheng Wei, Yao Hong, Zhilong Zhu, Xin Zhou
Summary: A visually meaningful double-image encryption scheme is proposed based on 2D compressive sensing and multi-rule DNA encoding. The plain images are first processed through scrambling, diffusing, and 2D compressive sensing to obtain privacy images, which are then re-encrypted using DNA encoding theory to obtain secret images. The secret images are embedded into carrier image wavelet coefficients, and the obtained result is processed to obtain a visually meaningful encrypted image. The scheme utilizes 2D compressive sensing to reduce data size and DNA encoding theory to enhance security.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Optics
Songzhi Tian, Lizhi Chen, Hao Zhang
Summary: In this study, the mechanisms of ringing artifacts in holographic projection are analyzed and a band-limited iterative algorithm is proposed to optimize the phase hologram. The method effectively suppresses ringing artifacts and speckle noise in lensless holographic projection.
Article
Optics
Jiasheng Xiao, Wenhui Zhang, Hao Zhang
Summary: This study provides a systematic analysis of sampling in Fresnel diffraction from the perspective of phase space optics, covering complex amplitude, intensity, amplitude, and phase. Practical suggestions are given and the analysis is verified through numerical experiments.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2022)
Article
Optics
Chengyu Wang, Minghao Hu, Yuzuru Takashima, Timothy Schulz, David Brady
Summary: In this study, convolutional neural networks are used to recover images that have been optically down-sampled by a factor of 6.7 using coherent aperture synthesis over a 16-camera array. Instead of relying on scanning and oversampling like conventional ptychography, decompressive neural estimation is applied here to recover full-resolution images from a single snapshot. Multiple snapshots can be used to improve the signal-to-noise ratio (SNR) as demonstrated in simulations. In addition, in-place training on experimental measurements eliminates the need for direct calibration of the measurement system. Simulations of various array camera sampling strategies are also presented to explore the optimization of snapshot compressive systems.
Editorial Material
Engineering, Electrical & Electronic
X. I. N. YUAN, D. A. V. I. D. J. BRADY, J. I. N. L. I. SUO, H. E. N. R. Y. ARGUELLO, M. I. G. U. E. L. RODRIGUES, A. G. G. E. L. O. S. K. KATSAGGELOS
Summary: This special section focuses on deep learning for high-dimensional sensing. Sensing is the first step for both humans and machines to understand the environment. High-dimensional sensing plays a crucial role in various fields, and the explosive growth of artificial intelligence has provided new opportunities and tools, especially in machine vision. Applications such as advanced driver assistance systems and autonomous driving systems require capturing and processing large-scale, high-dimensional, and diverse data in real-time with high accuracy. Therefore, it is important to develop high-performance sensing and processing techniques using recent advances in deep learning to handle high-dimensional data.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2022)
Article
Optics
Jiasheng Xiao, Wenhui Zhang, Hao Zhang
Summary: In this study, an exhaustive inverse diffraction analysis is presented from the perspective of phase space. It is shown that the forward and inverse diffraction processes in phase space are geometrically equivalent to the deformation and recovery of the phase space diagram. The symmetries of phase space transformations corresponding to different inverse diffraction forms are revealed, and the ambiguities between diffraction kernel conjugation and negative diffraction distance are clarified. The physical pictures of inverse diffraction are further given.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2023)
Article
Optics
Xiaoyu Wang, Hao Zhang
Summary: A digital micromirror device (DMD) is widely used in holographic display and light field manipulation due to its high-speed refresh rates. This study carefully analyzes the influence of the micromirror array structure of the DMD on wavefront control. By accurately describing the phase distribution along each micromirror surface direction and the distance between the micromirror and diffraction plane, the diffraction characteristics of the DMD are analyzed. The results show that the main order of the DMD with all micromirrors in the on state can be approximated as a plane wave, providing great convenience for computer holography based on DMD.
Article
Optics
Runze Zhu, Lizhi Chen, Hao Zhang
Summary: In this paper, a deep neural network with a physics-informed training strategy based on Fourier basis functions is proposed for phase hologram generation. The spatial frequency characteristics of the reconstructed diffraction fields can be regulated by recom-bining the Fourier basis functions in the frequency domain. Numerical and optical results demonstrate that the proposed method can effectively improve the generalization of the model with high-quality reconstructions.
Article
Optics
Tang Li, J. Lukas Dresselhaus, Nikolay Ivanov, Mauro Prasciolu, Holger Fleckenstein, Oleksandr Yefanov, Wenhui Zhang, David Pennicard, Ann-Christin Dippel, Olof Gutowski, Pablo Villanueva-Perez, Henry N. Chapman, Sasa Bajt
Summary: Scanning Compton X-ray microscopy allows for high-resolution imaging of dried, unstained, and unfixed biological samples. The use of novel wedged multilayer Laue lenses and efficient pixel-array detectors enables imaging at a resolution of about 70 nm, with minimal radiation damage. This technique has the potential to provide radiation damage-free images of biological samples at a resolution below 10 nm.
LIGHT-SCIENCE & APPLICATIONS
(2023)
Proceedings Paper
Optics
Jiasheng Xiao, Wenhui Zhang, Hao Zhang
Summary: This study presents a systematic analysis on the sampling of the wavefield and intensity in H-NEDs from the perspective of phase space optics. The evolution of the space-bandwidth product of the wavefield and intensity is provided, along with the sampling criteria and minimum number of samples. Such comprehensive sampling analysis provides guidance for the correct numerical calculation of diffraction fields in H-NEDs.
HOLOGRAPHY, DIFFRACTIVE OPTICS, AND APPLICATIONS XII
(2022)
Proceedings Paper
Optics
Lizhi Chen, Runze Zhu, Songzhi Tian, Hao Zhang
Summary: This paper proposes a stochastic gradient descent (SGD) algorithm with a weighted constraint strategy to solve the problem of vortex stagnation in computational holographic near-eye display and improve image quality. The weighted constraint strategy includes weighted phase constraint and weighted amplitude constraint. The algorithm ensures stable convergence of CGH optimization and eliminates speckles by smoothing the phase profile of the reconstructed field and introducing amplitude freedom in the non-signal region.
HOLOGRAPHY, DIFFRACTIVE OPTICS, AND APPLICATIONS XII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Lindsey Wiley, Joshua Follansbee, Patrick Leslie, Orges Furxhi, Rich Pimpinella, David Brady, Ronald Driggers
Summary: Long-range target identification has been extensively studied in the Visible, near-infrared, and short-wave infrared bands, but the extended short-wave infrared band shows superior performance in degraded visual environments.
INFRARED IMAGING SYSTEMS: DESIGN, ANALYSIS, MODELING, AND TESTING XXXIII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Charles J. Revello, Ronald G. Driggers, David Brady, Kyle Renshaw
Summary: The recent advancements in commercial drone performance have led to their increased use in private industries. Utilizing multiple lightweight sensors, these drones are able to achieve large area coverage while maintaining good ground sample resolution, offering a more robust system.
INFRARED IMAGING SYSTEMS: DESIGN, ANALYSIS, MODELING, AND TESTING XXXIII
(2022)
Review
Optics
Zhihong Zhang, Bo Zhang, Xin Yuan, Siming Zheng, Xiongfei Su, Jinli Suo, David J. Brady, Qionghai Dai
Summary: This paper discusses the application of computational imaging in snapshot compressive imaging (SCI) and semantic computer vision (SCV) tasks. The current practice of computational imaging has some limitations, including resource wastage, decreased efficiency, and reconstruction errors. To address these issues, the paper proposes a joint framework that combines SCV with SCI to leverage the advantages of both approaches.
