26 papers · ranked by Valyu relevance
Ming-Jie Sun, Ling-Tong Meng, Matthew P. Edgar, Miles J. Padgett + 1 more
'Neal Radwell'] Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition…
Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
—The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the…
Samuel Cahyawijaya
Biomedical image reconstruction research has been developed for more than five decades, giving rise to various techniques such as central and filtered back projection. With the rise of deep learning technology, biomedical image reconstruction field has undergone a massive paradigm shift from analytical and iterative…
Wieslaw Citko, Wieslaw Sienko
This paper considers the use of a machine learning system for the reconstruction and recognition of distorted or damaged patterns, in particular, images of faces partially covered with masks. The most up-to-date image reconstruction structures are based on constrained optimization algorithms and suitable regularizers.…
Ling-Qi Zhang, Nicolas P. Cottaris, David H. Brainard
We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…
Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa
Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that…
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Visual images perceived by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery remains a challenge. In this study, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level…
Leonid Yaroslavsky
The problem of minimization of the number of measurements needed for digital image acquisition and reconstruction with a given accuracy is addressed. Basics of the sampling theory are outlined to show that the lower bound of signal sampling rate sufficient for signal reconstruction with a given accuracy is equal to the…
Gengsheng L. Zeng
From time to time, it is necessary to determine whether there are sufficient measurements for the image reconstruction task especially when a non-standard scanning geometry is used. When the imaging system can be approximately modeled as a system of linear equations, the condition number of the system matrix indicates…
Jeffrey A. Fessler
At ICASSP 2017, I participated in a panel on "Open Problems in Signal Processing" led by Yonina Eldar and Alfred Hero. Afterwards the editors of the IEEE Signal Processing Magazine asked us to write a "perspectives" column on this topic. I prepared the text below but later found out that equations or citations are not…
Guy Gaziv, Roman Beliy, Niv Granot, Assaf Hoogi + 3 more
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We present a novel self-supervised approach that goes well beyond the…
Hanene Ben Yedder, Ben Cardoen, Ghassan Hamarneh
Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases. Reconstruction algorithms entail transforming signals collected by…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Jiachen Wu, Hua Zhang, Wenhui Zhang, Guofan Jin + 2 more
'George Barbastathis'] Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact, lightweight imaging systems. However, a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio. Current implementations require…
Authors not listed
nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface (GUI). Prioritising transparency and ease…
Hongli Shi, Shuqian Luo
Background The Filtered Back-Projection (FBP) algorithm is the most important technique for computerized tomographic (CT) imaging, in which the ramp filter plays a key role. FBP algorithm had been derived using the continuous system model. However, it has to be discretized in practical applications, which necessarily…
Balamurali Murugesan, Vijaya Raghavan S, Kaushik Sarveswaran, Keerthi Ram + 1 more
'Keerthi Ram' 'Mohanasankar Sivaprakasam'] Abstract. Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and softtissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In…
Guohua Shen, Tomoyasu Horikawa, Kei Majima, Yukiyasu Kamitani
Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases (1; 2) or to the matching to exemplars (3; 4). Recent work showed that visual cortical…
Rafał Stojek, Anna Pastuszczak, Piotr Wróbel, Magdalena Cwojdzińska + 6 more
'Kacper Sobczak' 'Rafał Kotyński' 'Qing Yu' 'Ran Tu' 'Ting Liu' 'Lina Li'] We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid…
Ling-Tong Meng, Ping Jia, Hong-Hai Shen, Ming-Jie Sun + 3 more
'Han-Yu Wang' 'Chun-Hui Yan'] Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured…
Mohammad Majid al-Rifaie, Tim Blackwell
This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates.…
K. Seeliger, U. Güçlü, L. Ambrogioni, Y. Güçlütürk + 1 more
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimUli presented during two functional magnetic resonance imaging experiments. Using a…
Hongyang Dong, Simon D.M. Jacques, Keith T. Butler, Olof Gutowski + 4 more
In this study, we introduce a method designed to eliminate parallax artefacts present in Xray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as…
Joel Bauer, Troy W. Margrie, Claudia Clopath
The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain sees. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction…
Authors not listed
Battery research increasingly relies on advanced imaging, yet open access to such data remains rare, scattered across various sources, and difficult to find. The Battery Imaging Library (BIL) is the first open, curated collection of multi-modal and multi-length scale battery imaging datasets, accompanied by a…