Coded Aperture Research Articles

Study of the Spatial Distribution of X-Pinch Plasma Radiation Using a New-Type Coded Aperture

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 via unsupervised lightweight local-global networks with geometric characteristics

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.

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism

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.

Spectral Reconstruction Improvement in a Cycloidal Coded-Aperture Mass Spectrometer.

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%.

Light-field photography using differential high-speed aperture coding

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.

Assessment of the axial resolution of a compact gamma camera with coded aperture collimator

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.

Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information.

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.

Optical Imaging Using Coded Aperture Correlation Holography (COACH) with PSF of Spatial-Structured Longitudinal Light Beams—A Study Review

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.

Interferenceless Incoherent Holography Optimized by Alternate Iteration of Binary Coded Phase Mask

Interferenceless Incoherent Holography Optimized by Alternate Iteration of Binary Coded Phase Mask

Learning Time-multiplexed phase-coded apertures for snapshot spectral-depth imaging.

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.

Coded aperture snapshot hyperspectral light field tomography.

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.

Coded aperture compression temporal imaging based on a dual-mask and deep denoiser.

Coded aperture compressive temporal imaging (CACTI) is the mapping of multiple frames using different encoding patterns into a single measurement and then using an algorithm to reconstruct the required high-dimensional signals, thus enabling high-speed photography on low-speed cameras. An encoding pattern and a reconstruction algorithm both play a critical role for CACTI. To improve the quality of the reconstruction, in terms of encoding, we took advantage of the reflective properties of the digital micromirror device and used a complementary dual-mask pattern to obtain more projection information. In terms of decoding, we developed what we believe, to the best of our knowledge, is a new model combining the weighted Landweber regularization with the relaxation strategy and a deep denoiser. The experimental results show the superiority of our proposed encoding-decoding combination, which achieves better performance in terms of the peak SNR, structural similarity index measure, and visual effects.

Compressive Sensing Based Active Imaging System Using Programable Coded Mask and a Photodiode

Compressive Sensing Based Active Imaging System Using Programable Coded Mask and a Photodiode

D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral image. However, these optical architectures suffer from a trade-off between the spatial and spectral resolution of the reconstructed image due to technology limitations. To overcome this issue, compressive spectral image fusion (CSIF) employs the projected measurements of two CSI architectures with different resolutions to estimate a high-spatial high-spectral resolution. This work presents the fusion of the compressive measurements of a low-spatial high-spectral resolution coded aperture snapshot spectral imager (CASSI) architecture and a high-spatial low-spectral resolution multispectral color filter array (MCFA) system. Unlike previous CSIF works, this paper proposes joint optimization of the sensing architectures and a reconstruction network in an end-to-end (E2E) manner. The trainable optical parameters are the coded aperture (CA) in the CASSI and the colored coded aperture in the MCFA system, employing a sigmoid activation function and regularization function to encourage binary values on the trainable variables for an implementation purpose. Additionally, an unrolling-based network inspired by the alternating direction method of multipliers (ADMM) optimization is formulated to address the reconstruction step and the acquisition systems design jointly. Finally, a spatial-spectral inspired loss function is employed at the end of each unrolling layer to increase the convergence of the unrolling network. The proposed method outperforms previous CSIF methods, and experimental results validate the method with real measurements.

Deep learning method for pinhole array color image reconstruction.

The following paper proposes a combination of a supervised encoder-decoder neural network with coded apertures. Coded apertures provide improved sensitivity and signal-to-noise ratio (SNR) in planar images. The unique array design of this method overcomes the spatial frequency cutoff found in standard multi-pinhole arrays. In this design, the pinholes were positioned to minimize loss in spatial frequencies. The large number of pinholes results in significant overlapping on the detector. To overcome the overlapping issue, reconstruction of the object from the obtained image is done using inverse filtering methods. However, traces of duplications remain leading to a decline in SNR, contrast, and resolution. The proposed technique addresses the challenge of image distortion caused by the lack of accuracy in the inverse filter methods, by using a deep neural network. In this work, the coded aperture is combined with a deep convolutional neural network (CNN) to remove noise caused by pinhole imaging and inverse filter limitations. Compared to only using Wiener filtering, the proposed method delivers higher SNR, contrast, and resolution. The imaging system is presented in detail with experimental results that illustrate its efficiency.

Curvature Sensing-Based Pupil Alignment Method for Large-Aperture Telescopes

In optical systems, pupil alignment is an important component of wavefront sensing, closed-loop feedback, and imaging vignetting control because it directly affects the detection limit of the system and thus the realization of scientific goals. This study proposes the use of the energy transfer characteristics of the pupil edge to determine the misalignment of each pupil. The mechanism involves decoupling the influence of each pupil from the final exit pupil energy distribution by using aperture coding without the addition of other optical paths. The degree to which the pupil is aligned is characterized by the normalized point source sensitivity (PSSn). The pupil misalignment PSSn increased from 0.75 to 0.83. The results of this study can be used to perform tomographic detection of pupil alignment and obtain high-quality telescopic images.

Compressive Spectral Imaging via Misalignment Induced Equivalent Grayscale Coded Aperture

Coded aperture snapshot spectral imager (CASSI) senses the spectral information of a 2D scene and captures a set of coded measurement data that can be used to reconstruct the 3D spatio-spectral datacube of the input scene by compressive sensing algorithms. The coded aperture (CA) in CASSI plays a crucial role in modulating the spatial information. The pixels in CA are typically square, switched binary on-off, and aligned with the pixels of focal plane array (FPA). Instead of this binary modulation, this paper explores a simple yet effective approach to enabling an equivalent grayscale modulation, which can increase the sensing degree of freedom in CASSI systems. In particular, we deliberately introduce misalignment between the CA pixels and the FPA pixels, such that the spatial modulation of one FPA pixel is determined by four adjacent CA pixels instead of one. Numerical experiments show that the proposed equivalent grayscale modulation induced by misalignment can significantly improve the CASSI reconstruction when compared with current methods, whether a random CA or an optimal blue noise CA is used. More importantly, it does not incur in any cost to the CASSI system.

Time-Division Multiplexing Light Field Display with Learned Coded Aperture.

Conventional stereoscopic displays suffer from vergence-accommodation conflict and cause visual fatigue. Integral-imaging-based displays resolve the problem by directly projecting the sub-aperture views of a light field into the eyes using a microlens array or a similar structure. However, such displays have an inherent trade-off between angular and spatial resolutions. In this paper, we propose a novel coded time-division multiplexing technique that projects encoded sub-aperture views to the eyes of a viewer with correct cues for vergence-accommodation reflex. Given sparse light field sub-aperture views, our pipeline can provide a perception of high-resolution refocused images with minimal aliasing by jointly optimizing the sub-aperture views for display and the coded aperture pattern. This is achieved via deep learning in an end-to-end fashion by simulating light transport and image formation with Fourier optics. To our knowledge, this work is among the first that optimize the light field display pipeline with deep learning. We verify our idea with objective image quality metrics (PSNR, SSIM, and LPIPS) and perform an extensive study on various customizable design variables in our display pipeline. Experimental results show that light fields displayed using the proposed technique indeed have higher quality than that of baseline display designs.

Coded aperture compressive temporal imaging using complementary codes and untrained neural networks for high-quality reconstruction.

The coded aperture compressive temporal imaging (CACTI) modality is capable of capturing dynamic scenes with only a single-shot of a 2D detector. In this Letter, we present a specifically designed CACTI system to boost the reconstruction quality. Our design is twofold: for the optical encoder, we use complementary codes instead of random ones as widely adopted before; for the reconstruction algorithm, an untrained neural network-based algorithm is developed. Experimental and simulation tests show that such co-design of encoding-decoding produces superior image quality over other CACTI schemes using random codes and other optimization algorithms. In addition, a dual-prism design in the optical system improves the light efficiency by approximately a factor of four compared with previous systems.

Enhancement of Imaging Quality of Interferenceless Coded Aperture Correlation Holography Based on Physics-Informed Deep Learning

Interferenceless coded aperture correlation holography (I-COACH) was recently introduced for recording incoherent holograms without two-wave interference. In I-COACH, the light radiated from an object is modulated by a pseudo-randomly-coded phase mask and recorded as a hologram by a digital camera without interfering with any other beams. The image reconstruction is conducted by correlating the object hologram with the point spread hologram. However, the image reconstructed by the conventional correlation algorithm suffers from serious background noise, which leads to poor imaging quality. In this work, via an effective combination of the speckle correlation and neural network, we propose a high-quality reconstruction strategy based on physics-informed deep learning. Specifically, this method takes the autocorrelation of the speckle image as the input of the network, and switches from establishing a direct mapping between the object and the image into a mapping between the autocorrelations of the two. This method improves the interpretability of neural networks through prior physics knowledge, thereby remedying the data dependence and computational cost. In addition, once a final model is obtained, the image reconstruction can be completed by one camera exposure. Experimental results demonstrate that the background noise can be effectively suppressed, and the resolution of the reconstructed images can be enhanced by three times.