Incremental sampling can be applied in scientific imaging techniques whenever the measurements are taken incrementally, i.e., one pixel position is measured at a time. It can be used to reduce the measurement time as well as the dose impinging onto a specimen. For incremental sampling, the choice of the sampling pattern plays a major role in order to achieve a high reconstruction quality. Besides using

Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization

In this work we investigate the practicality of stochastic gradient descent and its variants with variance-reduction techniques in imaging inverse problems. Such algorithms have been shown in the large-scale optimization and machine learning literature to have optimal complexity in theory, and to provide great improvement empirically over the deterministic gradient methods. However, in some tasks such

Though deep learning based spectral super-resolution (SSR) methods have state-of-the-art performances, most previous deep spectral super-resolution approaches require extensive paired RGB images and hyperspectral images (HSIs) for well-fitting learning. However, in real cases, the cost of generating such paired images is too prohibitive to collect sufficient training samples. To solve this problem

Coherent imaging methods like synthetic aperture radar (SAR) are subject to speckle, and the suppression of this noise-like quality is often considered a prerequisite to image interpretation. Likewise, circumstances such as frequency jamming or system multiplexing produce gaps in the data and corresponding image artifacts, which further impact the utility of resulting imagery and induce correlation

Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere. While methods exist for removing such turbulent distortions, many are limited to static sequences which cannot be extended to dynamic scenes. In addition, the physics of the turbulence is often not integrated into the image reconstruction algorithms, making the physics foundations

Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical physics-based reconstructions. Termed physics-based networks, they incorporate both the known physics of the system via its forward model, and the power of deep learning via data-driven training. However, for realistic

In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, whereas its size leads to a high memory-demand. Both of these aspects can be limiting factors in practice. In order to reduce measurement time, compressed sensing exploits the

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with low-dose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object. In a TTV model, the sparsity

Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acquisition of multispectral images, with high spatial but low spectral resolution, and hyperspectral images, with low spatial but high spectral resolution. To enhance scientific interpretation of the data, we propose a data fusion

The use of microwave tomography (MWT) in an industrial drying process is demonstrated in this feasibility study with synthetic measurement data. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam during the microwave drying process. Such moisture information is crucial in developing control strategies for controlling the microwave power for selective

Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but the success of these supervised methods critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising

As a non-invasive and radiation-free imaging modality, electrical impedance tomography (EIT) has attracted much attention in the field of industrial measurement. However, image reconstruction with EIT is a non-linear and ill-posed inverse problem, causing it to suffer from low spatial resolution and high noise sensitivity. To overcome this problem, a point-constrained framework is proposed to guide

For compressed sensing magnetic resonance imaging (CS-MRI) that utilize sparse representations, the regularization parameter establishes a trade-off between sparsity and data fidelity. While convergence to the desired solution is slow for mean squared error (MSE) optimal constant regularization, continuation using decreasing parameter values enables faster convergence. To derive an explicit rule for

The range frequency autocorrelation function (RFAF) based algorithm is proposed for radar target detection and motion parameter estimation in our previous work. In the RFAF-based method, the symmetric autocorrelation function (SAF) is constructed with respect to the range frequency, and three dimensional (slow time, range frequency, and shift frequency) energy accumulation can be completed coherently

In conventional ultrasound (US) imaging, it is common to transmit several focused beams at multiple locations to generate a multi-focus image with constant lateral resolution throughout the image. However, this method comes at the expense of a loss in temporal resolution, which is important in applications requiring both high-frame rate and constant lateral resolution. Moreover, relative motions of

Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced run-time complexity. These neural networks are usually trained in a supervised manner, so matched pairs of subsampled, and fully sampled $k$ -space data are required. Unfortunately, it is often difficult to acquire matched fully sampled

Discrete delta functions define the limits of attainable spatial resolution for all imaging systems. Here we construct broad, multi-dimensional discrete functions that replicate closely the action of a Dirac delta function for convolution under aperiodic boundary conditions. These arrays spread the energy of a sharp probe beam to simultaneously sample multiple points across the volume of a large object

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this article, we propose a new type of convolutional

Although lowering X-ray radiation helps reduce the risk of cancer in patients, low-dose computed tomography (LDCT) technology usually leads to poor image quality, such as amplified mottle noise and streak artifacts, which severely impact the diagnostic results. To improve diagnostic performance, we propose an algorithm based on a cycle-consistent generative adversarial network (CycleGAN) to suppress

The principle of quantitative acoustic microscopy (QAM) is to form two-dimensional (2D) acoustic parameter maps from a collection of radiofrequency (RF) signals acquired by raster scanning a biological sample. Despite their relatively simple structure consisting of two main reflections, RF signals are currently sampled at very high frequencies, e.g. , at 2.5 GHz for QAM system employing a single-element

It is possible to generate stereo high dynamic range (HDR) images/videos by using a pair of cameras with different exposure parameters. In this article, a learning-based stereo HDR imaging (SHDRI) method with three modules is proposed. In the proposed method, we construct three convolutional neural network (CNN) modules that perform specific tasks, including exposure calibration CNN (EC-CNN) module

Hyperspectral recovery using RGB images has recently attracted considerable attention in many imaging and computer vision applications because of its ability to equip a low cost tool in acquiring spectral signatures of natural scenes. Current methods of recovering hyperspectral information via RGB measurements may fail for objects sharing similar RGB features. In this paper, we introduce a novel framework

The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computer vision is the optimal transport framework. Its ability to model geometric displacements across an image's support gives it attractive qualities similar to optical flow methods that are effective at capturing visual motion, but are restricted to operate in significantly

Fusion based hyperspectral image (HSI) super-resolution method, which obtains a spatially high-resolution (HR) HSI by fusing a low-resolution (LR) HSI and an HR conventional image, has been a prevalent method for HSI super-resolution. One effective fusion based method is to cast HSI super-resolution into a unified optimization problem, where handcrafted priors such as sparse prior or low rank prior

A full 360 $^\circ$ × 180 $^\circ$ image provides an unlimited field of view (FOV) and an immersive experience for the users without any loss of information of the surrounding. In this study, a deep learning based approach is proposed to synthesize a 360 $^\circ$ image from a sparse set of images captured with a limited FOV. The proposed network consists of a cascade of the FOV estimation network and

A multistage algorithm for quantitative microwave breast imaging is presented which utilizes the eigenfunction-based reconstruction of the complex-valued permittivity as prior information. The eigenfunction-based reconstruction is obtained from a single-frequency non-iterative microwave inversion technique that uses the eigenfunctions of the Helmholtz operator, in a resonant conductive enclosure, as

Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse

In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this article, we tackle both problems simultaneously for the specific case of 2D Cartesian sampling, using a novel end-to-end learning framework that we call LOUPE (Learning-based

In recent years, photoacoustic image reconstruction has received extensive attention. Various reconstruction methods, such as back-projection, frequency domain reconstruction, time reversal, and model-based reconstructions have been developed. Although these methods are based on different propagation theories, they have relatively simple implementations when reconstructing images on homogenous media

At high frequencies, phase measurement is difficult to meet the accuracy required for imaging, and it is more susceptible to noise pollution. Consequently, it is important to develop inversion models and algorithms for electromagnetic inverse scattering problems with phaseless data (PD-ISPs). Compared to the ISPs with full-data (FD-ISPs), the PD-ISPs are more nonlinear due to the lack of the phase

Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low rank tensor ring has been applied in tensor completion. To further deal with its sensitivity to sparse component as it does in tensor principle component analysis, we propose robust tensor ring completion (RTRC), which separates

Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a fast and high performance alternative. Unfortunately, the CNN approaches usually

Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs). However, there has been limited technical development focusing on single hyperspectral image super-resolution due to the high-dimensional and complex spectral patterns

Pixelated source-mask joint optimization (SMO) plays a crucial role in improving the resolution and image fidelity of optical lithography process. However, the ever growing integration density of semiconductor devices incurs the explosion of data throughput, and poses a considerable challenge on the computational efficiency of pixelated SMO algorithms. This paper proposes to use compressive sensing

Phase-shifting 3D profilometry is of high precision, but its performance could be far below expectations due to its long exposure time in the measurement of dynamic objects, especially human faces. In this article, a new idea is proposed to improve the measuring speed by providing reference derived from the statistical characteristics of human faces for the reconstruction of faces measured. During

In this work we introduce a new Radon transform which arises from a new modality of Compton Scattering Tomography (CST). This new system is made of a single detector rotating around a fixed source. Unlike some previous CST, no collimator is used at the detector. Such a system allows us to collect scattered photons coming from two opposite sides of the source-detector segment, hence the manifold of

An original 3-D high resolution radar imaging approach, called SPRITE for “SParse Radar Imaging TEchnique,” is presented. It incorporates in an original way the available prior knowledge about the electromagnetic backscattering, extending the commonly used sparse point-wise scatterers to sparse facet-wise scatterers. It is based on a regularization scheme that accounts for information of sparsity and

Light-field microscopy (LFM) is a type of all-optical imaging system that is able to capture 4D geometric information of light rays and can reconstruct a 3D model from a single snapshot. In this paper, we propose a new 3D localization approach to effectively detect 3D positions of neuronal cells from a single light-field image with high accuracy and outstanding robustness to light scattering. This

In this article, we present a novel Bayesian approach for estimating spectral and range profiles from single-photon Lidar waveforms associated with single surfaces in the photon-limited regime. In contrast to classical multispectral Lidar signals, we consider a single Lidar waveform per pixel, whereby a single detector is used to acquire information simultaneously at multiple wavelengths. A new observation

Multispectral imaging systems are increasingly used in many scientific fields. However multispectral images generally present spectral and spatial limitations: the spectral information within each band is lacking because of spectral integration over the band, and the spatial resolution is limited due to the spatial convolution by spectrally variant Point Spread Functions which introduce a spatial variant

Sparse-view and limited-angle Computed Tomography (CT) are very challenging problems in real applications. Due to the high ill-posedness, both analytical and iterative reconstruction methods may present distortions and artifacts for such incomplete data problems. In this work, we propose a novel reconstruction model to jointly reconstruct a high-quality image and its corresponding high-resolution projection

We consider the problem of reconstructing an image from its noisy measurements using a prior specified only with an image denoiser. Recent work on plug-and-play priors (PnP) and regularization by denoising (RED) has shown the state-of-the-art performance of image reconstruction algorithms under such priors in a range of imaging problems. In this work, we develop a new block coordinate RED algorithm

Template matching (TM) is a common methodology for target detection (TD) which allows detecting a target based on cross-correlation analysis between a reference pattern and the scene. State-of-the-art TD approaches do not consider the optical phase of the target as a discriminant in the detection process, because to recover the phase involves solving a computationally demanding inverse problem known

Dynamic analysis of in-orbit satellites is of interest for space situation applications. Some exploratory methods have been presented to analyze the state of attitude-steadied satellites in the complex space situation, but effectively estimating the dynamic parameters of spinning satellites remains a significant and interesting challenge nowadays. With decoupling target spin estimation from its trajectory

Blind binocular visual quality prediction (BVQP) is more challenging than blind monocular visual quality prediction (MVQP). Recently, the application of convolutional neural networks (CNNs) to blind MVQP has resulted in significant progress in that area. In contrast, the adoption of deep learning for blind BVQP has received scant attention. In this study, we devised an end-to-end deep fusion network

Synthetic aperture radar (SAR) has extensive applications in both civilian and military fields for its ability to create high-resolution images of the ground target without being affected by weather conditions and daytime or nighttime effect. As fueled by the several decades’ advancement of SAR, existing SAR systems exhibit high imaging capabilities, namely, significantly high two-dimensional resolution

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Binary tomography is concerned with the recovery of binary images from a few of their projections (i.e., sums of the pixel values along various directions). To reconstruct an image from noisy projection data, one can pose it as a constrained least-squares problem. As the constraints are nonconvex, many approaches for solving it rely on either relaxing the constraints or heuristics. In this paper, we

In this paper, we present a full view optical flow estimation method for plenoptic imaging. Our method employs the structure delivered by the four-dimensional light field over multiple views making use of superpixels. These superpixels are four dimensional in nature and can be used to represent the objects in the scene as a set of slanted-planes in three-dimensional space so as to recover a piecewise

In micro-tomography, variants of helical X-ray source trajectories, e.g., double-helix (DH) and space-filling (SF), are attractive alternatives to the conventional circular trajectory, as they satisfy data-sufficiency conditions; this enables exact reconstruction, and large cone-angle (or high flux) imaging. Geometric alignment of micro-tomography experimental data, i.e., radiographs or projection

Removing the undesired reflections of images taken through glass is an important problem in digital photography and many other vision applications. The so-called ghosting effect, i.e., the pattern repetitiveness in reflection, is an effective cue used by existing techniques to remove reflection from images. Existing methods take a two-stage approach that first estimates the parameters of ghosting effect

In this paper, we introduce a deep-learning-based framework to solve electromagnetic inverse scattering problems. This framework builds on and extends the capabilities of existing physics-based inversion algorithms. These algorithms, such as the contrast source inversion, subspace-optimization method, and their variants face a problem of getting trapped in false local minima when recovering objects

Real-world data processing problems often involve various image modalities associated with a certain scene, including RGB images, infrared images, or multispectral images. The fact that different image modalities often share certain attributes, such as edges, textures, and other structure primitives, represents an opportunity to enhance various image processing tasks. This paper proposes a new approach

Coded aperture X-ray computed tomography (CAXCT) systems reconstruct high quality images of the inner structure of an object from a few coded illumination measurements. Since the computed tomography (CT) system matrix is highly structured, random coded apertures lead to lower quality image reconstructions. In this paper, the noisy forward models of CAXCT in both Gaussian noise and Poisson noise are

In this paper, we consider imaging the reflectivity of scatterers from intensity-only data recorded by a single moving transducer that both emits and receives signals, forming a synthetic aperture. By exploiting frequency illumination diversity, we obtain multiple intensity measurements at each location, from which we determine field cross correlations using an appropriate phase controlled illumination

When a narrowband coherent wavefront passes through or reflects off of a scattering medium, the input and output relationship of the incident field is linear and so can be described by a transmission matrix (TM). If the TM for a given scattering medium is known, one can computationally “invert” the scattering process and image through the medium. In this paper, we investigate the effect of broadband

In this paper, we consider the problem of vectorvalued regularization of light fields based on PDEs. We propose a regularization method operating in the four-dimensional (4-D) ray space that does not require prior estimation of disparity maps. The method performs a PDE-based anisotropic diffusion along directions defined by local structures in the 4-D ray space. We analyze light field regularization

Photon counting detection is a promising approach toward dose reduction in X-ray computed tomography (CT). Full CT reconstruction from a fraction of the detected photons required by energy-integrating detectors has been demonstrated. In medical and industrial CT applications, projection truncation to the region-of-interest (ROI) is another effective way of dose reduction, as information from the ROI

The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3-D) imaging require specialized algorithms. In this paper, a new reconstruction algorithm for single-photon 3-D Lidar images is presented that can deal with multiple tasks. For example, when the return signal contains multiple peaks due to imaging semitransparent surfaces, or when imaging through