Search Results (108)

Snapshot compressive imaging (SCI), including coded aperture compressive temporal imaging (CACTI), reconstructs high-speed video frames from compressed low-frame-rate measurements. Most deep SCI reconstruction networks are designed around a measurement-wise formulation: each compressed exposure is reconstructed independently, and the resulting frame segments are concatenated to form a continuous video. This protocol is effective for within-measurement reconstruction, but it leaves cross-measurement temporal continuity largely unmodeled. Boundary artifacts such as flickering, texture drift, or motion jumps can therefore appear between adjacent reconstructed segments, even when frame-wise reconstruction metrics remain competitive. This work identifies and empirically analyzes the underexplored problem of cross-measurement temporal continuity in continuous SCI, and it provides TCA-EfficientSCI as a lightweight, causal, and reproducible baseline. The Temporal Context Adapter uses the last m reconstructed frames from the previous measurement as causal temporal context and injects this history through a gated residual feature pathway. A boundary consistency loss regularizes the predicted temporal variation across measurement boundaries without forcing adjacent frames to be identical. In a controlled three-seed comparison, Full TCA with boundary loss reduces mean Boundary Difference Error (BDE) by 2.23% relative to the matched-epoch EfficientSCI control while maintaining similar PSNR and SSIM. Correct-history inference gives BDE 0.01615, while zero and shuffled history give 0.01725 and 0.01810, respectively. The adapter adds 1,019,905 parameters, or 11.56% relative to the EfficientSCI baseline parameters, and it changes $256\times 256$ mean latency from 54.35 ms to 68.58 ms per measurement in the profiling protocol. Rather than claiming broad reconstruction-quality improvement, this study highlights cross-measurement continuity as an important evaluation and design dimension for continuous SCI deployment. Full article

Video Compressive Imaging (VCI) enables low-dimensional detectors to capture high-dimensional data through incoherent encoding. However, traditional pseudo-random coding often exhibits structural sampling that leads to detail loss. While adjusting the sampling rate can balance structured sampling and incoherence, the reconstruction quality remains unsatisfactory. To overcome this limitation, we propose a high-frequency coding method that mitigates the structural problems of pseudo-random coding by reducing low-frequency components. Simulation results show that this method significantly improves image detail reconstruction, with an average peak signal-to-noise ratio (PSNR) increase of 1.6% across various sampling rates. At a 20% sampling rate, the PSNR improvement reaches around 6%. Furthermore, the method integrates easily into existing VCI systems, offering substantial improvements in image reconstruction quality and reliability compared to pseudo-random coding. Full article

Recently, artificial neural networks (ANNs) have shown impressive performance in the compressive hyperspectral imaging (CHI) reconstruction task, but the high energy consumption limits their deployment on energy-constrained devices. This paper develops a novel spiking neural network (SNN), termed spiking spectral-weighting reconstruction network (SSWR-Net), to significantly improve the energy–efficiency ratio in CHI reconstruction. Firstly, a spiking spectral-weighting convolution block is proposed to adaptively modulate the spiking signals, enabling the SNN to fit continuous spectral correlation curves. Secondly, a residual feature reuse module with more direct connections is designed to achieve efficient and lightweight spatial–spectral feature extraction. Thirdly, customized feature scaling architectures are introduced to resolve the dimensional mismatch issue and enhance information flow. Finally, we propose a novel temporal-wise progressive training method to optimize the multi-timestep SSWR-Net, which can significantly improve both training efficiency and reconstruction quality. Both simulation and real experiments demonstrate the superiority of the proposed method in both CHI reconstruction performance and energy efficiency. Specifically, SSWR-Net outperforms its ANN-based counterpart by 0.87 dB at a 19.74% energy cost. Full article

The macroscopic Fourier ptychography (FP) is regarded as a highly promising approach of creating a synthetic aperture for macro visible imaging to achieve sub-diffraction-limited resolution. However most existing macro FP techniques rely on the high-precision translation stage to drive laser or camera scanning, thereby increasing system complexity and bulk. Meanwhile, the scanning process is slow and time-consuming, hindering the ability to achieve rapid imaging. In this paper, we introduce an innovative illumination scheme that employs a spatial light modulator to achieve precise programmable variable-angle illumination at a relatively long distance, and it can also freely adjust the illumination spot size through phase coding to avoid the issues of limited field of view and excessive dispersion of illumination energy. Coupled with a camera array, this could significantly reduce the number of shots taken by the imaging system and enable a lightweight and highly efficient solid-state macro FP imaging system with a large equivalent aperture. The effectiveness of the method is experimentally validated using various optically rough diffuse objects and a USAF target at laboratory-scale distances. Full article

Coded Aperture Snapshot Spectral Imaging (CASSI) is a rapid hyperspectral imaging technique with broad application prospects. Due to limitations in three-dimensional compressed data acquisition modes and hardware constraints, the compressed measurements output by actual CASSI systems have a finite dynamic range, leading to degraded hyperspectral reconstruction quality. To address this issue, a high-quality hyperspectral reconstruction method based on multi-exposure fusion is proposed. A multi-exposure data acquisition strategy is established to capture low-, medium-, and high-exposure low-dynamic-range (LDR) measurements. A multi-exposure fusion-based high-dynamic-range (HDR) CASSI measurement reconstruction network (HCNet) is designed to reconstruct physically consistent HDR measurement images. Unlike traditional HDR networks for visual enhancement, HCNet employs a multiscale feature fusion architecture and combines local–global convolutional joint attention with residual enhancement mechanisms to efficiently fuse complementary information from multiple exposures. This makes it more suitable for CASSI systems, ensuring high-fidelity reconstruction of hyperspectral data in both spatial and spectral dimensions. A multi-exposure fusion CASSI mathematical model is constructed, and a CASSI experimental system is established. Simulation and real-world experimental results demonstrate that the proposed method significantly improves hyperspectral image reconstruction quality compared to traditional single-exposure strategies, exhibiting high robustness against multi-exposure interval jitters and shot noise in practical systems. Leveraging the higher-dynamic-range target information acquired through multiple exposures, especially in HDR scenes, the method enables reconstruction with enhanced contrast in both bright and dark details and also demonstrates higher spectral correlation, validating the enhancement of CASSI reconstruction and effective measurement capability in HDR scenarios. Full article

Coded aperture X-ray computed tomography (CAXCT) measures coded X-ray projections to reconstruct the inner structure of an object. Coded apertures, which determine the point spread function, can be designed to improve the reconstruction quality, but most approaches are computationally expensive, leading to very small images. In this paper, a sparse covariance matrix estimation approach is introduced to minimize the information loss sensed by projections corresponding to large tomographic images. The covariance matrix representing the map of the overlapping information of the projections is obtained by using block matrix multiplication and sparse estimation. A heuristic variant algorithm with a noise factor is presented to search the combinations of D projections leading to maximum non-overlapping information acquisition, where D is the number of unblocking elements on the coded apertures. Numerical experiments with simulated datasets show that the optimization performance of the proposed method is comparable to that of state-of-the-art methods with small images. Further, for the analyzed cases, coded aperture optimization was performed with $512 \times 512$ images by analyzing coefficients smaller than $0.02\%$ in the covariance matrix. Full article

Radar waveform design is an important approach to radar system performance enhancement. For a long time, synthetic aperture radar (SAR) systems have utilized linear frequency modulation (LFM) waveforms as transmitted signals and have relied on window functions to suppress sidelobes. However, this approach significantly degrades system signal-to-noise ratio (SNR) and resolution. Nonlinear frequency modulation (NLFM) waveforms can suppress sidelobes without SNR loss and have been widely applied in the SAR field in recent years. Nonetheless, they still cannot completely avoid resolution loss. To address this, this article, based on an advanced High-Freedom Parameterized Frequency Modulation (HFPFM) coding model, constructs a waveform sidelobe optimization model constrained by mainlobe widening and solves it using a gradient descent method. Through detailed experiments, we found that the optimized waveform, compared to the LFM waveform, can reduce sidelobes by more than 9 dB without widening the mainlobe, thereby simultaneously avoiding the resolution and SNR losses caused by window function weighting. In addition, this optimization method can efficiently and rapidly optimize all parameters simultaneously using only matrix multiplication and fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT). The SAR point target imaging simulation results verify that the optimized waveform can clearly image weak targets near strong targets, which proves the effectiveness of the proposed method. Full article

Coded-Aperture Snapshot Spectral Imaging (CASSI) systems acquire 3D spatial–spectral information on dynamic targets by converting 3D hyperspectral images (HSIs) into 2D compressed measurements. Various end-to-end networks have been proposed for HSI reconstruction from these measurements. However, these methods have not explored the frequency-domain information of HSIs. This research presents the spatial/spectral-frequency adaptive network (SSFAN) for CASSI image reconstruction. A frequency-division transformation (FDT) decomposes HSIs into distinct Fourier frequency components, enabling multiscale feature extraction in the frequency domain. The proposed dual-branch architecture consists of a spatial–spectral module (SSM) to preserve spatial–spectral consistency and a frequency division module (FDM) to model inter-frequency dependencies. Channel compression/expansion modules are integrated into the FDM to balance computational efficiency and reconstruction quality. Frequency-division loss supervises feature learning across divided frequency channels. Ablation experiments validate the contributions of each network module. Furthermore, comparison experiments on synthetic and real CASSI datasets demonstrate that SSFAN outperforms state-of-the-art end-to-end methods in reconstruction performance. Full article

To design novel imaging devices, we use masks coded with numerical sequences. These masks work in conjunction with varifocal systems that implement zero-throw tunable magnification. Some masks control field depth, even when the size of the pupil aperture remains fixed. Pairs of vortex masks are used to implement tunable phase radial profiles, like axicons and lenses. The autocorrelation properties of the Barker sequences are applied to the generation of narrow passband windows on the OTF. For this application, we apply Barker matrices in rectangular coordinates. A similar procedure, but now in polar coordinates, is useful for sensing in-plane rotations. We implement geometrical transformations by using zero-throw, tunable, anamorphic magnifications. Full article

This paper reveals a counterintuitive, non-monotonic dependence of terahertz coded-aperture imaging (TCAI) performance on the imaging range. This phenomenon stems from phase-induced spatiotemporal correlations in the reference-signal matrix (RSM), governed by the wavefront phase interactions between the coded-aperture elements and scatterers on the imaging plane. Image quality deteriorates noticeably when a specific dimensionless criterion, which is defined mathematically and physically in this work, precisely reaches integer values. Under such conditions, the relative phase difference concentrates or clusters into discrete values determined by the imaging range, leading to strong column and row correlations in RSM that compromise the spatiotemporal independence essential for high-quality reconstruction. For imaging ranges exceeding the critical threshold determined by the number of grid points along one dimension of the imaging plane, two degradation mechanisms emerge: increased correlation between RSM columns mapping to directly adjacent scatterers and phase coverage reduction in wavefront encoding. Both effects intensify as the imaging range increases, resulting in a monotonic deterioration of imaging performance. Crucially, reconstruction fails primarily when strong correlations involve dominant scatterers, whereas correlations among non-dominant (dummy) scatterers have a negligible impact. The Two-step Iterative Shrinkage/Thresholding (TwIST) algorithm demonstrates superior robustness under these challenging conditions compared to some other conventional methods. These insights provide practical guidance for optimizing TCAI system design and operational range selection to avoid performance degradation zones. Full article

Coded aperture imaging (CAI) is a powerful imaging technology that has rapidly developed during the past decade. CAI technology and its integration with incoherent holography have led to the development of several cutting-edge imaging tools, devices, and techniques with widespread interdisciplinary applications, such as in astronomy, biomedical sciences, and computational imaging. In this review, we provide a comprehensive overview of the recently developed CAI techniques in the framework of incoherent digital holography. The review starts with an overview of the milestones in modern CAI technology, such as interferenceless coded aperture correlation holography, followed by a detailed survey of recently developed CAI techniques and system designs in subsequent sections. Each section provides a general description, principles, potential applications, and associated challenges. We believe that this review will act as a reference point for further advancements in CAI technologies. Full article

In this article, the fast algorithms for the discrete Tchebichef transform (DTT) are proposed for input sequences of lengths in the range from 3 to 8. At present, DTT is widely applied in signal processing, image compression, and video coding. The review of the articles related to fast DTT algorithms has shown that such algorithms are mainly developed for input signal lengths 4 and 8. However, several problems exist for which signal and image processing with different apertures is required. To avoid this shortcoming, the structural approach and a sparse matrix factorization are applied in this paper to develop fast real DTT algorithms for short-length input signals. According to the structural approach, the rows and columns of the transform matrix are rearranged, possibly by changing the signs of some rows or columns. Next, the matched submatrix templates are extracted from the matrix structure and decomposed into a matrix product to construct the factorization of an initial matrix. A sparse matrix factorization assumes that the butterfly architecture can be extracted from the transform matrix. Combining the structural approach with a sparse matrix factorization, we obtained the matrix representation with reduced computational complexity. Based on the obtained matrix representation, the fast algorithms were developed for the real DTT via the data flow graphs. The fast algorithms for integer DTT can be easily obtained using the constructed data flow graphs. To confirm the correctness of the designed algorithms, the MATLAB R2023b software was applied. The constructed factorizations of the real DTT matrices reduce the number of multiplication operations by 78% on average compared to the direct matrix-vector product at signal lengths in the range from 3 to 8. The number of additions decreased by 5% on average within the same signal length range. Full article

Microwave computational imaging (MCI) based on coded apertures does not rely on relative motion between the radar platform and the target, enabling forward-looking imaging. The performance of MCI depends on the computational methods and modulation of the coded aperture, particularly its design. However, current research methods treat the optimization of the coded aperture and computational imaging processing as independent tasks, with no unified framework to link these two aspects, limiting the potential for improving system performance. This paper proposes a novel deep learning-based MCI framework that jointly optimizes the coded aperture and image reconstruction process. Unlike traditional methods that decouple these two stages, our approach trains the sensing and reconstruction networks in an end-to-end fashion. The key novelty lies in constructing an end-to-end imaging network based on a convolutional neural network (CNN) where the coded aperture is modeled as a convolutional layer within the network. Physical constraints on the coded aperture are enforced by adding regularizers to the loss function. Simulation experiments demonstrate that under low signal-to-noise ratio (SNR) and low compression ratio conditions, the proposed method improves peak signal-to-noise ratio (PSNR) by 5 dB to 8 dB, enhances SSIM by 10% to 15%, and reduces relative imaging error by 0.5% to 1%. Full article

Accurate near-surface wind speed and direction measurements are crucial for validating atmospheric models, especially for the purpose of adequately assessing the interactions between the surface and wind, which in turn results in characteristic vertical profiles. Coastal regions pose unique challenges due to the discontinuity between land and sea and the complex interplay of atmospheric stability, topography, and boundary/layer dynamics. This study focuses on a unique database of wind profiles collected over several years at a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) coastal site in the southern Italian region of Calabria (Lamezia Terme, code: LMT). By leveraging remote sensing technologies, including wind lidar combined with in situ measurements, this work comprehensively analyzes wind circulation at low altitudes in the narrowest point of the entire Italian peninsula. Seasonal, daily, and hourly wind profiles at multiple heights are analyzed, highlighting the patterns and variations induced by land–sea interactions. A case study integrating Synthetic Aperture Radar (SAR) satellite images and in situ observations demonstrates the importance of multi-sensor approaches in capturing wind dynamics and validating model simulations. Data analyses demonstrate the occurrence of extreme events during the winter and spring seasons, linked to synoptic flows; fall seasons have variable patterns, while during the summer, low-speed winds and breeze regimes tend to prevail. The prevailing circulation is of a westerly nature, in accordance with other studies on large-scale flows. Full article

Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms. Full article