# IEEE Transactions on Computational Imaging

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### RAISR: Rapid and Accurate Image Super Resolution

Publication Year: 2017, Page(s):110 - 125
Cited by:  Papers (50)
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Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the single image super-resolution problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e., a mapping) that when applied to given image that is not in the training set,... View full abstract»

• ### Loss Functions for Image Restoration With Neural Networks

Publication Year: 2017, Page(s):47 - 57
Cited by:  Papers (77)
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Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is ℓ2. In this paper, we bring attention to alternative ... View full abstract»

• ### Video Super-Resolution With Convolutional Neural Networks

Publication Year: 2016, Page(s):109 - 122
Cited by:  Papers (71)
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Convolutional neural networks (CNN) are a special type of deep neural networks (DNN). They have so far been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of video super-resolution. We propose a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution. Cons... View full abstract»

• ### Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior

Publication Year: 2019, Page(s):37 - 51
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Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed-forward neural networks have led to state-of-the-art results in solving various inverse imaging problems. However, being task specific, these networks have to be learned for each inverse problem. On the other hand, a more flexible approach would be to ... View full abstract»

• ### Video Superresolution via Motion Compensation and Deep Residual Learning

Publication Year: 2017, Page(s):749 - 762
Cited by:  Papers (9)
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Video superresolution (SR) techniques are of essential usages for high-resolution display devices due to the current lack of high-resolution videos. Although many algorithms have been proposed, video SR still remains a very challenging inverse problem under different conditions. In this paper, we propose a new method for video SR named motion compensation and residual net (MCResNet). We use optica... View full abstract»

• ### Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask

Publication Year: 2018, Page(s):298 - 310
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Depth estimation from a single image is a well-known challenge in computer vision. With the advent of deep learning, several approaches for monocular depth estimation have been proposed, all of which have inherent limitations due to the scarce depth cues that exist in a single image. Moreover, these methods are very demanding computationally, which makes them inadequate for systems with limited pr... View full abstract»

• ### Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model

Publication Year: 2019, Page(s):17 - 26
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The low-rank plus sparse (L+S) decomposition model enables the reconstruction of undersampled dynamic parallel magnetic resonance imaging data. Solving for the low rank and the sparse components involves nonsmooth composite convex optimization, and algorithms for this problem can be categorized into proximal gradient methods and variable splitting methods. This paper investigates new efficient alg... View full abstract»

• ### Regularization by Denoising: Clarifications and New Interpretations

Publication Year: 2019, Page(s):52 - 67
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Regularization by denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. Experimental evidence suggests that the RED algorithms are a state of the art. We claim, however, that explicit regularization does not explain the RED algorithms. ... View full abstract»

• ### Sensing Matrix Design via Capacity Maximization for Block Compressive Sensing Applications

Publication Year: 2019, Page(s):27 - 36
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It is well-established in the compressive sensing (CS) literature that sensing matrices whose elements are drawn from independent random distributions exhibit enhanced reconstruction capabilities. In many CS applications, such as electromagnetic imaging, practical limitations on the measurement system prevent one from generating sensing matrices in this fashion. Although one can usually randomize ... View full abstract»

• ### Convolutional Dictionary Learning: A Comparative Review and New Algorithms

Publication Year: 2018, Page(s):366 - 381
Cited by:  Papers (3)
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Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters. While effective algorithms have recently been developed for the convolutional sparse coding problem, the corresponding dictionary learning problem is substantially more challenging. Furthermore, although a number of differen... View full abstract»

• ### A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Publication Year: 2016, Page(s):480 - 495
Cited by:  Papers (40)
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Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on a novel observat... View full abstract»

• ### FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation

Publication Year: 2017, Page(s):384 - 397
Cited by:  Papers (6)
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FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera, where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded mea... View full abstract»

• ### Joint SAR Imaging and Multi-Feature Decomposition From 2-D Under-Sampled Data Via Low-Rankness Plus Sparsity Priors

Publication Year: 2019, Page(s):1 - 16
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In this paper, we introduce a multi-feature decomposition approach to the problem of synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range and azimuth directions. Conventional SAR image formation methods may produce images that are not appropriate for interpretation tasks such as segmentation and automatic target recognition. We deal with this problem using an e... View full abstract»

• ### Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images

Publication Year: 2018, Page(s):326 - 340
Cited by:  Papers (1)
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Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a data-driven noniterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution,ReconNet, is a deep neural network, which is learned end-to-end to map block-wise compressive measurements of the scene to ... View full abstract»

• ### Colored Coded Aperture Design in Compressive Spectral Imaging via Minimum Coherence

Publication Year: 2017, Page(s):202 - 216
Cited by:  Papers (4)
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Colored coded aperture optimization in compressive spectral imaging is discussed. Based on the analysis of the coherence of the underlying sensing matrix, a general family of codes is derived. These designs lead to reconstructions of multispectral scenes of better quality than the ones obtained using the traditional random black and white coded apertures. The approach used in this work exploits th... View full abstract»

• ### Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications

Publication Year: 2017, Page(s):84 - 98
Cited by:  Papers (43)
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Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving constrained optimization problems in image restoration. Among many useful features, one critical feature of the ADMM algorithm is its modular structure, which allows one to plug in any off-the-shelf image denoising algorithm for a subproblem in the ADMM algorithm. Because of the plug-in nature, this type of AD... View full abstract»

• ### X-Ray Ghost-Tomography: Artefacts, Dose Distribution, and Mask Considerations

Publication Year: 2019, Page(s):136 - 149
Cited by:  Papers (1)
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Ghost imaging has recently been successfully achieved in the X-ray regime. Due to the penetrating power of X-rays this immediately opens up the possibility of ghost-tomography. No research into this topic currently exists in the literature. Here, we present adaptations of conventional X-ray tomography techniques to this new ghost-imaging scheme. Several numerical implementations for tomography thr... View full abstract»

• ### Lensless Imaging With Compressive Ultrafast Sensing

Publication Year: 2017, Page(s):398 - 407
Cited by:  Papers (6)
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Lensless imaging is an important and challenging problem. One notable solution to lensless imaging is a single-pixel camera that benefits from ideas central to compressive sampling. However, traditional single-pixel cameras require many illumination patterns that result in a long acquisition process. Here, we present a method for lensless imaging based on compressive ultrafast sensing. Each sensor... View full abstract»

• ### Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors

Publication Year: 2015, Page(s):112 - 125
Cited by:  Papers (43)
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Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using fixed dwell time per pixel and on the order of one ... View full abstract»

• ### A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging

Publication Year: 2017, Page(s):445 - 459
Cited by:  Papers (7)
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Conventional LIDAR systems require hundreds or thousands of photon detections per pixel to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in th... View full abstract»

• ### Optical Tomographic Image Reconstruction Based on Beam Propagation and Sparse Regularization

Publication Year: 2016, Page(s):59 - 70
Cited by:  Papers (26)
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Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear counterparts, but they generally result in computationally prohibitive reconstruction algorithms. Although sparsity-driven regularizers significantl... View full abstract»

• ### Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging

Publication Year: 2019, Page(s):109 - 119
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An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. The proposed modification introduces an extra term, which is a multiple of the proximal-gradient step, into the so-called momentum formula used for ... View full abstract»

• ### Orthogonal Coded Active Illumination for Millimeter Wave, Massive-MIMO Computational Imaging With Metasurface Antennas

Publication Year: 2018, Page(s):184 - 193
Cited by:  Papers (1)
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Emerging metasurface antenna technology enables flexible and low cost massive multiple-input multiple-output (MIMO) millimeter-wave (mmW) imaging for applications such as personnel screening, weapon detection, reconnaissance, and remote sensing. This work proposes an orthogonal coded active illumination (OCAI) approach which utilizes simultaneous, mutually orthogonal coded transmit signals to illu... View full abstract»

• ### Exploiting Occlusion in Non-Line-of-Sight Active Imaging

Publication Year: 2018, Page(s):419 - 431
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Active non-line-of-sight imaging systems are of growing interest for diverse applications. The most commonly proposed approaches to date rely on exploiting time-resolved measurements, i.e., measuring the time it takes for short-duration light pulses to transit the scene. This typically requires expensive, specialized, ultrafast lasers, and detectors that must be carefully calibrated. We develop an... View full abstract»

• ### An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

Publication Year: 2019, Page(s): 1
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Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the proximal gradient method (PGM). The proposed algo... View full abstract»

• ### Gradient-Based Source Mask Optimization for Extreme Ultraviolet Lithography

Publication Year: 2019, Page(s):120 - 135
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Extreme ultraviolet (EUV) lithography is the most promising technology for the next generation very-large scale integrated circuit fabrication. EUV lithography invariably introduces distortions in the projected lithographic mask patterns and thus inverse lithography tools are needed to compensate for these. This paper develops two kinds of model-based source and mask optimization (SMO) frameworks,... View full abstract»

• ### Sensing Matrix Design via Mutual Coherence Minimization for Electromagnetic Compressive Imaging Applications

Publication Year: 2017, Page(s):217 - 229
Cited by:  Papers (13)
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Compressive sensing (CS) theory states that sparse signals can be recovered from a small number of linear measurements y = Ax using l<sub>1</sub>norm minimization techniques, provided that the sensing matrix satisfies a restricted isometry property (RIP). Unfortunately, the RIP is difficult to verify in electromagnetic imaging applications, where the sensing matrix is computed determin... View full abstract»

• ### Multi-Exposure Image Fusion by Optimizing A Structural Similarity Index

Publication Year: 2018, Page(s):60 - 72
Cited by:  Papers (3)
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We propose a multi-exposure image fusion (MEF) algorithm by optimizing a novel objective quality measure, namely the color MEF structural similarity (MEF-SSIM<sub>c</sub>) index. The design philosophy we introduce here is substantially different from existing ones. Instead of pre-defining a systematic computational structure for MEF (e.g., multiresolution transformation and transform d... View full abstract»

• ### SEAGLE: Sparsity-Driven Image Reconstruction Under Multiple Scattering

Publication Year: 2018, Page(s):73 - 86
Cited by:  Papers (1)
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Multiple scattering of an electromagnetic wave as it passes through an object is a fundamental problem that limits the performance of current imaging systems. In this paper, we describe a new technique-called Series Expansion with Accelerated Gradient Descent on the Lippmann-Schwinger Equation-for robust imaging under multiple scattering based on a combination of an iterative forward model and a t... View full abstract»

• ### Unified Image Fusion Framework With Learning-Based Application-Adaptive Importance Measure

Publication Year: 2019, Page(s):82 - 96
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This paper presents a novel unified image fusion framework based on an application-adaptive importance measure. In the proposed framework, an important area is selected using the importance measure obtained for each image type in each application. The key is to learn this application-adaptive importance measure that can select the important area irrespective of the input image type without manuall... View full abstract»

• ### A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recovery

Publication Year: 2017, Page(s):535 - 550
Cited by:  Papers (4)
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Fourier-domain structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation and wavelet regularization. These priors specify that a convolutional structured matrix, i.e., Toeplitz, Hankel, or their multilevel generalizations, built from Fourier data of the image should be low-rank. The main challenge in applying these schemes... View full abstract»

• ### 3-D Level Set Method for Joint Contrast and Shape Recovery in Microwave Imaging

Publication Year: 2019, Page(s):97 - 108
Cited by:  Papers (1)
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We propose a three-dimensional (3-D) fast level set method, which can estimate both object shape and dielectric contrast with reduced computational cost. In prior work, we presented a 2-D fast level set method that integrated the level set inversion within the Born iterative method (BIM). This approach significantly reduced the computational cost; however, it was limited to estimating object shape... View full abstract»

• ### Deconvolution and Restoration of Optical Endomicroscopy Images

Publication Year: 2018, Page(s):194 - 205
Cited by:  Papers (1)
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Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ molecular signatures of disease such as infection and inflammation. However,a enhancing the quality of data acquired by this technique for better visualization and subsequent analysis remains a challenging ... View full abstract»

• ### Model-Based Iterative Reconstruction for One-Sided Ultrasonic Nondestructive Evaluation

Publication Year: 2019, Page(s):150 - 164
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One-sided ultrasonic nondestructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform, and use an algorithm to reconstruct the quantity of ... View full abstract»

• ### Generating High-Resolution Image and Depth Map Using a Camera Array With Mixed Focal Lengths

Publication Year: 2019, Page(s):68 - 81
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Producing high-resolution images and depth maps of a scene is critical to many applications. Although some imaging systems today are equipped with multiple cameras, the output image resolution is usually only a small fraction of the total number of sensor pixels. To significantly increase the output pixel ratio, we propose an imaging system that consists of an array of telescopic cameras and a wid... View full abstract»

• ### Travel Time Tomography With Adaptive Dictionaries

Publication Year: 2018, Page(s):499 - 511
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We develop a two-dimensional travel time tomography method, which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness maps. This patch regularization, called the local model, is integ... View full abstract»

• ### Refocusing and Motion Parameter Estimation for Ground Moving Targets Based on Improved Axis Rotation-Time Reversal Transform

Publication Year: 2018, Page(s):479 - 494
Cited by:  Papers (4)
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In this paper, a new algorithm is presented to image ground moving targets and estimate their motion parameters in a synthetic aperture radar system based on improved axis rotation-time reversal transform (IAR-TRT). In this algorithm, the second-order Keystone transform) is applied to correct the range curvature, where the Doppler ambiguity caused by a fast-moving target is considered. Then, resid... View full abstract»

• ### Object Depth Profile and Reflectivity Restoration From Sparse Single-Photon Data Acquired in Underwater Environments

Publication Year: 2017, Page(s):472 - 484
Cited by:  Papers (3)
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This paper presents two new algorithms for the joint restoration of depth and reflectivity (DR) images constructed from time-correlated single-photon counting measurements. Two extreme cases are considered: 1) a reduced acquisition time that leads to very low photon counts; and 2) imaging in a highly attenuating environment (such as a turbid medium), which makes the reflectivity estimation more di... View full abstract»

• ### Reconstructing Video of Time-Varying Sources From Radio Interferometric Measurements

Publication Year: 2018, Page(s):512 - 527
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Very long baseline interferometry (VLBI) makes it possible to recover the images of astronomical sources with extremely high angular resolution. Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes. Interferometry provides measuremen... View full abstract»

• ### Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging

Publication Year: 2019, Page(s): 1
| | PDF (8305 KB) |  Media

Coded-illumination can enable quantitative phase microscopy of transparent samples with minimal hardware requirements. Intensity images are captured with different source patterns, then a non-linear phase retrieval optimization reconstructs the image. The non-linear nature of the processing makes optimizing the illumination pattern designs complicated. Traditional techniques for experimental desig... View full abstract»

• ### Spectral Super-Resolution in Colored Coded Aperture Spectral Imaging

Publication Year: 2016, Page(s):440 - 455
Cited by:  Papers (4)
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Colored coded apertures have been recently introduced in compressive spectral imaging as a method to improve the quality of image reconstructions in terms of signal to noise ratio. This paper shows that colored coded apertures, in addition, can also provide a higher number of resolvable spectral bands. Colored coded apertures with real and ideal spectral responses are both considered. The maximum ... View full abstract»

• ### Space-Filling X-Ray Source Trajectories for Efficient Scanning in Large-Angle Cone-Beam Computed Tomography

Publication Year: 2018, Page(s):447 - 458
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We present a new family of X-ray source scanning trajectories for large-angle cone-beam computed tomography. Traditional scanning trajectories are described by continuous paths through space, e.g., circles, saddles, or helices, with a large degree of redundant information in adjacentprojectionimages. Here, we consider discrete trajectories as a set of points that uniformly sample ... View full abstract»

• ### A Joint Row and Column Action Method for Cone-Beam Computed Tomography

Publication Year: 2018, Page(s):599 - 608
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The inversion of linear systems is fundamental in computed tomography (CT) reconstruction. Computational challenges arise when trying to invert large linear systems, as limited computing resources mean that only a part of the system can be kept in computer memory at any one time. In linear tomographic inversion problems, such as X-ray tomography, even a standard scan can produce millions of indivi... View full abstract»

• ### Adaptive Sparse Image Sampling and Recovery

Publication Year: 2018, Page(s):311 - 325
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This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler str... View full abstract»

• ### Simultaneous Projector-Camera Self-Calibration for Three-Dimensional Reconstruction and Projection Mapping

Publication Year: 2017, Page(s):74 - 83
Cited by:  Papers (6)
| | PDF (804 KB) | HTML

Automatic calibration of structured-light systems, generally consisting of a projector and camera, is of great importance for a variety of practical applications. We propose a novel optimization approach for geometric calibration of a projector-camera system that estimates the intrinsic, extrinsic, and distortion parameters of both the camera and projector in an automatic fashion using structured ... View full abstract»

• ### A Parametric Level set Method for Imaging Multiphase Conductivity Using Electrical Impedance Tomography

Publication Year: 2018, Page(s):552 - 561
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Electrical impedance tomography (EIT) is an imaging modality that provides cross-sectional images of objects that carry contrasts in electrical conductivity. EIT is suitable for example for monitoring industrial processes involving multiple phases with different conductivities. This paper presents a parametric level set (PLS) based reconstruction scheme for an EIT-imaging of conductivity distribut... View full abstract»

• ### Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms Using Gamma Markov Random Fields

Publication Year: 2017, Page(s):658 - 670
Cited by:  Papers (3)
| | PDF (939 KB) | HTML

This paper presents a new Bayesian spectral unmixing algorithm to analyze remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e., on the target reflectivity). B... View full abstract»

• ### Face Detection and Verification Using Lensless Cameras

Publication Year: 2019, Page(s): 1
| | PDF (31459 KB) |  Media

Camera-based face detection and verification have advanced to the point where they are ready to be integrated into myriad applications, from household appliances to Internet of Things (IoT) devices to drones. Many of these applications impose stringent constraints on the form-factor, weight, and cost of the camera package that cannot be met by current-generation lens-based imagers. Lensless imagin... View full abstract»

• ### Mixed Integer Programming for Sparse Coding: Application to Image Denoising

Publication Year: 2019, Page(s): 1
| | PDF (509 KB)

Dictionary learning for sparse representations is generally conducted in two alternating steps: sparse coding and dictionary updating. In this paper, a new approach to solve the sparse coding step is proposed. Because this step involves a $\ell_o$-norm, most, if not all existing solutions only provide a local or approximate solution. Instead, a real \$\el... View full abstract»

• ### View-Consistent MeshFlow for Stereoscopic Video Stabilization

Publication Year: 2018, Page(s):573 - 584
| | PDF (4314 KB) | HTML Media

This paper presents a method to stabilize shaky stereoscopic videos captured by hand-held stereo cameras. It is often problematic to apply a traditional monocular video stabilization techniques directly to the stereoscopic views independently. This is mainly because some undesirable vertical disparities and inaccurate horizontal disparities are produced, which violates the original stereoscopic di... View full abstract»

## Aims & Scope

The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Dr. Brendt Wohlberg
Los Alamos National Laboratory