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Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in pra

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm th

The theory and techniques of compressed sensing (CS) have shown their potential as a breakthrough in accelerating k-space data acquisition for parallel magnetic resonance imaging (pMRI). However, the performance of CS reconstruction models in pMRI has not been fully maximized, and CS recovery guarantees for pMRI are largely absent. To improve reconstruction accuracy from parsimonious amounts of k-space data while maintaining flexibility, a new CS SENSitivity Encoding (SENSE) pMRI reconstruction

Longitudinal evaluation using diffusion-weighted imaging and collision event monitoring was performed on high school athletes who participate in American football. Observed changes in white matter health were suggestive of injury and found to be correlated with accumulation of head collision events during practices and games.

Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis

Convolutional analysis operator learning (CAOL) methods train an autoencoding convolutional neural network (CNN) in an unsupervised learning manner, to more accurately solve inverse problems. Block Proximal Gradient method using a Majorizer (BPG-M) achieved fast and convergent CAOL, by using sharp majorizers and the momentum terms. This paper proposes a model-based image reconstruction (MBIR) method using autoencoding CNNs trained via CAOL, for sparse-view computational tomography (CT). We apply

Convolutional dictionary learning (CDL) has great potential to “learn” rich sparse representations from training datasets, by training translation-invariant filters. However, the performance of applying learned filters from CDL to inverse problems has not yet been fully maximized because training data preprocessing in training stage is not fully compensated in testing stage. We propose CDL using Adaptive Contrast Enhancement (CDL-ACE) that additionally models the preprocessing in CDL, and image

PURPOSE: Dual-energy computed tomography (DECT) has widely been used in many applications that need material decomposition. Image-domain methods directly decompose material images from high- and low-energy attenuation images, and thus, are susceptible to noise and artifacts on attenuation images. The purpose of this study is to develop an improved iterative neural network (INN) for high-quality image-domain material decomposition in DECT, and to study its properties. METHODS: We propose a new IN

Magnetic resonance imaging (MRI) is considered a key modality for the future as it offers several advantages, including the use of non-ionizing radiation and having no known side effects on the human body, and has recently begun to serve as a key component of multi-modal neuroimaging. However, two major intrinsic problems exist: slow acquisition and intrusive acoustic noise. Parallel MRI (pMRI) techniques accelerate acquisition by reducing the duration and coverage of conventional gradient encod

In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This paper 1) incorporates image mapping CNN using identical convolutional kernels in both encoders and decoders into a block coordinate descent (BCD) signal recovery method and 2) applies

Parallel coils at both the transmitter and receiver can be used to offer more control over the magnetic resonance imaging (MRI) system, and this implementation has potential to improve the performance in high-field MRI. A new MSE-based EXcitation Pattern (MSE-EXP) design for image reconstruction in parallel transmit and receive SENSitivity Encoding (pTxRx SENSE) MRI is presented in this paper to maximize the performance of an MRI using an array of transmit and receive coils. In the the small-tip

Many practical sensing applications involve multiple sensors simultaneously acquiring measurements of a single object. Conversely, most existing sparse recovery guarantees in compressed sensing concern only single-sensor acquisition scenarios. In this paper, we address the optimal recovery of compressible signals from multi-sensor measurements using compressed sensing techniques. This confirms the benefits of multi-over single-sensor environments in the sense of reducing the number of measuremen

Convolutional operator learning is increasingly gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called local approaches that extract and store many overlapping patches across training signals. Due to memory demands, local approaches have limitations when learning kernels from large datasets -- particularly with multi-layered structures, e.g., convolutional neural network (CNN) -- and/or applying the learned kernels to high-di