To improve the security and efficiency of multi-image encryption, this paper proposes a hybrid encryption method that combines Interferenceless Coded Aperture Correlation Holography (I-COACH) with chaotic modulation and compressed sensing techniques. The method constructs a dual-layer encryption framework, integrating optical and digital processing to overcome the limitations of single-domain schemes.<br>In the optical layer, I-COACH is employed to encode multiple input images by recording their point spread holograms without interference, providing initial encryption and resistance against physical attacks. The resulting hologram is then processed using block-wise Discrete Cosine Transform (DCT) to achieve sparsity. Dual chaotic sequences perturb DCT coefficients to enhance key sensitivity and randomness. Finally, compressed sensing is applied to achieve secondary encryption while reducing the data volume by 30%, enabling efficient and secure storage or transmission. Experimental results demonstrate that the proposed method achieves an average Number of Pixels Change Rate (NPCR) of 99.44% and a Unified Average Changing Intensity (UACI) of 33.04% against differential attacks, with a ciphertext entropy of 7.9996 bit. Moreover, it exhibits excellent encryption performance in terms of key sensitivity, robustness, and resistance to statistical analysis. This method provides a practical solution for secure image application scenarios such as medical imaging and surveillance.

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 images by analyzing coefficients smaller than in the covariance matrix.

Abstract We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism. It is based on our discovery that per-pixel object distance can be rigorously determined by a coupled pair of optical derivatives of a defocused image using a simple, closed-form relationship. Unlike previous depth-from-defocus (DfD) methods that leverage higher-order spatial derivatives of the image to estimate scene depths, the proposed mechanism’s use of only first-order optical derivatives makes it significantly more robust to noise. Furthermore, unlike many previous DfD algorithms with requirements on aperture code, this relationship is proved to be universal to a broad range of aperture codes. We build the first 3D sensor based on depth from coupled optical differentiation. Its optical assembly includes a deformable lens and a motorized iris, which enables dynamic adjustments to the optical power and aperture radius. The sensor captures two pairs of images: one pair with a differential change of optical power and the other with a differential change of aperture scale. From the four images, a depth and confidence map can be generated with only 36 floating point operations per output pixel (FLOPOP), more than ten times lower than the previous lowest passive-lighting depth sensing solution to our knowledge. Additionally, the depth map generated by the proposed sensor demonstrates more than twice the working range of previous DfD methods while using significantly lower computation.

Objective.The objective of the presented study was to evaluate the feasibility of a coded-mask (CM) gamma camera for real-time range verification in proton therapy, addressing the need for a precise and efficient method of treatment monitoring.Approach.A CM gamma camera prototype was tested in clinical conditions. The setup incorporated a scintillator-based detection system and a structured tungsten collimator. The experiment consisted of the irradiation of PMMA phantom with proton beams of energies ranging from 70.51 to 108.15 MeV. Experimental data were benchmarked against Monte Carlo simulations. The distal falloff position (DFP) was determined for both experimental data and simulations.Main results.The tested CM camera achieved a statistical precision of DFP determination of 1.7 mm for 108protons, which is consistent with simulation predictions, despite hardware limitations such as non-functional detector pixels. Simulations indicated that a fully operational setup would further improve the performance of the detector. The system demonstrated rate capability sufficient for clinical proton beam intensities and maintained performance without significant dead time.Significance.This study validates the potential of the CM gamma camera for real-time proton therapy monitoring. The technology promises to enhance treatment accuracy and patient safety, offering a competitive alternative to existing approaches such as single-slit and multi-slit systems.

A sparse coded mask modeling technique is proposed to increase the transmission capacity of an optical wireless link based on Li-Fi. The learning model for the discrete multitone (DMT) signal waveform is implemented using the proposed technique, which is designed based on a masked auto-encoder. The entire length of the DMT signal waveform, encoded using quadrature phase shift keying (QPSK) or 16-quadrature amplitude modulation (16-QAM) symbols, is divided into equal intervals to generate DMT patches, which are subsequently compressed based on the specified masking ratio. After 1-m optical wireless transmission, the DMT signal waveform is reconstructed from the received DMT patch through a decoding process and then QPSK or 16-QAM symbols are recovered. Using the proposed technique, we demonstrate that we can increase the transmission capacity by up to 1.85 times for a 10 MHz physical bandwidth. Additionally, we verify that the proposed technique is feasible in Li-Fi networks with illumination environments above 240 lux.

Abstract In this concept study, we explore coded aperture imaging as a high‐angular resolution imaging technique for suprathermal electron strahl observations in the solar wind. In particular, studying the relative contribution of pitch‐angle scattering to solar wind strahl broadening near 1 AU requires very high‐resolution observations of electron pitch angle. Coded aperture imaging is advantageous because it is a high‐signal method that can provide high‐angular resolution observations from a simple, and compact platform. In this study, we present an initial design concept to achieve a 40 field‐of‐view with 3.1 angular resolution from a CubeSat‐sized platform. We include an “egg‐crate” collimator design to mitigate the impact of the partially coded field‐of‐view as well as block solar photons. We also describe an estimate of the instrument data production and a possible CMOS candidate for low energy energetic particle detection. Finally, we present initial results of simulated strahl in Geant4 and the instrument response to these distributions. We find that reconstructed distributions can have accurate estimates of the strahl width. However, we find that especially for more broad strahl observations, coded aperture artifacts diminish the reconstruction quality and result in large deviations between input and output distributions. Possible options to improve accuracy include increasing integration time or reducing energy resolution.

Using Adaptive Genetic Algorithm Optimize to realize the Extended Depth-of-field Wavefront Coding Mask for a Large Field-of-view Microscopy System

Glow discharge optical emission spectrometry (GDOES) allows fast and simultaneous multielemental analysis directly from solids and depth profiling down to the nanometer scale, which is critical for thin-film (TF) characterization. Nevertheless, operating conditions for the best limits of detection (LODs) are compromised in lieu of the best sputtering crater shapes for depth resolution. In addition, the fast transient signals from ultra-TFs do not permit the optimal sampling statistics of bulk analysis such that LODs are further compromised. Furthermore, commercial GDOES instruments rely on a slit-based light dispersion that favors high spectral resolution at the expense of light throughput. Here, a new technique called glow discharge optical emission coded aperture spectrometry (GOCAS) is shown to allow both a higher spectral resolution and higher light throughput by using a coded aperture (CA) with multiple thin slits at the spectrograph's entrance to measure the convoluted spectra and compressed sensing (CS) algorithms to recover the deconvoluted spectra from the full field of view. The effects of CA characteristics on spectral reconstruction fidelity were studied and showed the best fidelity for smaller slits, 50% transmittance, and wider CA with a higher number of slits. In addition, Shearlet enhanced snapshot compressive imaging (SeSCI)GPU showed the best performance of the CS algorithms studied, including SeSCICPU, two-step iterative shrinkage/thresholding (TwIST), and alternating direction method of multipliers total variation minimization (ADMM-TV). Moreover, GOCAS is shown to be very robust against increasing detector Gaussian noise. Finally, standard reference materials are used to show up to ∼30× improved S/N and an order-of-magnitude improved LODs, at the fastest acquisition times (fraction of a ms), which has the potential to be transformative for depth profiling of nanostructured materials.

Optimized Coded Apertures for Hyperspectral Image Reconstruction via Variant RIP Constant

To study the spatial distribution of the intensity of an X-ray source of electric discharge plasma, a new-type coded aperture, which is a structure of intersecting mutually perpendicular transparent and opaque strips with the widths selected using a random number generator, has been used. The radiation passed through the coded aperture has produced a complex pattern of the coded image, which has been recorded on a Fuji TR fluorescent imaging plate without a protective coating. A mathematical procedure based on the iterative method of solving an incorrectly posed problem given by the Fredholm integral equation of the first kind has been applied to reconstruct the spatial distribution of plasma radiation intensity from this pattern. It has been shown that the use of the coded aperture not only has increased significantly the light intensity of the recording system in comparison with a pinhole camera, but also has made it possible to obtain a spatial resolution of the discharge plasma no worse than the resolution of the pinhole camera. The applicability of the developed iterative method for both sources close to point ones and extended emitting objects has been demonstrated.

Coded aperture compressive temporal imaging (CACTI) utilizes compressive sensing (CS) theory to compress three dimensional (3D) signals into 2D measurements for sampling in a single snapshot measurement, which in turn acquires high-dimensional (HD) visual signals. To solve the problems of low quality and slow runtime often encountered in reconstruction, deep learning has become the mainstream for signal reconstruction and has shown superior performance. Currently, however, impressive networks are typically supervised networks with large-sized models and require vast training sets that can be difficult to obtain or expensive. This limits their application in real optical imaging systems. In this paper, we propose a lightweight reconstruction network that recovers HD signals only from compressed measurements with noise and design a block consisting of convolution to extract and fuse local and global features, stacking multiple features to form a lightweight architecture. In addition, we also obtain unsupervised loss functions based on the geometric characteristics of the signal to guarantee the powerful generalization capability of the network in order to approximate the reconstruction process of real optical systems. Experimental results show that our proposed network significantly reduces the model size and not only has high performance in recovering dynamic scenes, but the unsupervised video reconstruction network can approximate its supervised version in terms of reconstruction performance.

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

Spatial aperture coding is a technique used to improve throughput without sacrificing resolution both in optical spectroscopy and sector mass spectrometry (MS). Previous work demonstrated that aperture coding combined with a position-sensitive array detector in a miniature cycloidal mass spectrometer was successful in providing high-throughput, high-resolution measurements. However, due to poor alignment and field nonuniformities, reconstruction artifacts were present. Recently, significant progress was made in eliminating most of the reconstruction artifacts with improved field uniformity and alignment. However, artifacts as large as 1/3 of the main peak were still observed at low mass (<17 u). Such artifacts will reduce accuracy in identification and quantification of analytes, reducing the impact of the throughput advantage gained by using a coded aperture. The artifacts were hypothesized to be a result of a mass dependent in curvature of ions in the ion source. Ions with higher mass (m/z > 17 u) and a larger curvature did not pass through all slits in the coded aperture. Therefore, when reconstructing with a system response derived from the aperture image from a higher mass m/z = 32 u ion, reconstruction artifacts appeared for m/z < 17 u. In this work, two methods were implemented to significantly reduce the presence of artifacts in reconstructed data. First, we modified the reconstruction algorithm to incorporate a mass-dependent system response function across the mass range (10-110 u). This method reduced the size of the artifacts by 82%. Second, to validate the hypothesis that the mass-dependent system response function was a result of differences in curvature of ions in the ion source, we modified the design of the ion source by shifting the coded aperture slits relative to the center of the ionization volume. This method resulted in ions of all masses passing through all slits in the coded aperture, a constant system response function across the entire mass range. Artifacts were reduced by 94%.

Programmable aperture light-field photography enables the acquisition of angular information without compromising spatial resolution. However, direct current (DC) background noise is unavoidable in images recorded by programmable aperture light-field photography, leading to reducing the contrast of reconstructed images. In addition, it requires sacrificing temporal resolution to obtain angular information, making it a challenge to capture dynamic scenes. In this paper, we propose programmable aperture light-field photography using differential high-speed aperture coding. This method effectively reduces DC noise and produces high-contrast refocused images. Furthermore, we build a light-field camera based on a 1250 Hz spatial light modulator and a 1250 fps high-speed camera, achieving dynamic light-field photography at 1110(H)×800(V) resolution and 24 fps. Our results demonstrate significant improvements in image contrast and exhibit considerable promise for diverse applications.

PurposeHandheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods.MethodsAn experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12–100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM).ResultsWhile taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively.ConclusionOur results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

We are currently investigating the ultrasound imaging of a sensor that consists of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for a puncture microscope application. The proposed model was conducted using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system that affects its resolution, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model resulting in a poor quality. In a previous work, the phase information was extracted from the echo signal to improve the image quality. This study proposes a strategy by integrating the weighted frequency subbands compound and a super-resolution technique to enhance the resolution in range and lateral direction. The image performance with different methods was also evaluated using the experimental data. The results indicate that better image resolution and speckle suppression were obtained by applying the proposed method.

Spatial-structured longitudinal light beams are optical fields sculpted in three-dimensional (3D) space by diffractive optical elements. These beams have been recently suggested for use in improving several imaging capabilities, such as 3D imaging, enhancing image resolution, engineering the depth of field, and sectioning 3D scenes. All these imaging tasks are performed using coded aperture correlation holography systems. Each system designed for a specific application is characterized by a point spread function of a different spatial-structured longitudinal light beam. This article reviews the topic of applying certain structured light beams for optical imaging.

提出基于交替策略的随机轨迹直接二进制搜索法。数值仿真结果表明,该方法有效抑制了迭代过程中直流偏置的增加并提高迭代速度。实验结果证明,在同样的迭代次数下,该方法显著降低了重建图像的相关重建噪声。该方法推动了无干涉编码孔径相关全息术(I-COACH)系统在可见光范围外成像的应用。您的浏览器不支持 audio 元素。AI语音播报

Depth and spectral imaging are essential technologies for a myriad of applications but have been conventionally studied as individual problems. Recent efforts have been made to optically encode spectral-depth (SD) information jointly in a single image sensor measurement, subsequently decoded by a computational algorithm. The performance of single snapshot SD imaging systems mainly depends on the optical modulation function, referred to as codification, and the computational methods used to recover the SD information from the coded measurement. The optical modulation has been conventionally realized using coded apertures (CAs), phase masks, prisms or gratings, active illumination, and many others. In this work, we propose an optical modulation (codification) strategy that employs a color-coded aperture (CCA) in conjunction with a time-varying phase-coded aperture and a spatially-varying pixel shutter, thus yielding an effective time-multiplexed coded aperture (TMCA). We show that the proposed TMCA entails a spatially-variant point spread function (PSF) for a constant depth in a scene, which, in turn, facilitates the distinguishability, and therefore, better recovery of the depth information. Further, the selective filtering of specific spectral bands by the CCA encodes relevant spectral information that is disentangled using a reconstruction algorithm. We leverage the advances of deep learning techniques to jointly learn the optical modulation and the computational decoding algorithm in an end-to-end (E2E) framework. We demonstrate via simulations and with a real testbed prototype that the proposed TMCA strategy outperforms state-of-the-art snapshot SD imaging alternatives in both spectral and depth reconstruction quality.

Multidimensional imaging has emerged as a powerful technology capable of simultaneously acquiring spatial, spectral, and depth information about a scene. However, existing approaches often rely on mechanical scanning or multi-modal sensing configurations, leading to prolonged acquisition times and increased system complexity. Coded aperture snapshot spectral imaging (CASSI) has introduced compressed sensing to recover three-dimensional (3D) spatial-spectral datacubes from single snapshot two-dimensional (2D) measurements. Despite its advantages, the reconstruction problem remains severely underdetermined due to the high compression ratio, resulting in limited spatial and spectral reconstruction quality. To overcome this challenge, we developed a novel two-stage cascaded compressed sensing scheme called coded aperture snapshot hyperspectral light field tomography (CASH-LIFT). By appropriately distributing the computation load to each stage, this method utilizes the compressibility of natural scenes in multiple domains, reducing the ill-posed nature of datacube recovery and achieving enhanced spatial resolution, suppressed aliasing artifacts, and improved spectral fidelity. Additionally, leveraging the snapshot 3D imaging capability of LIFT, our approach efficiently records a five-dimensional (5D) plenoptic function in a single snapshot.