LOFAR sparse image reconstruction [PDF]
Astronomy & Astrophysics, 2015 Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various ...
A. W. Gunst +89 more
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Image reconstruction in optical interferometry [PDF]
IEEE Signal Processing Magazine, 2010 Comment: accepted for publication in IEEE Signal Processing ...
Thiébaut, Eric +1 more
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On Hallucinations in Tomographic Image Reconstruction [PDF]
IEEE Transactions on Medical Imaging, 2020 Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property.
Sayantan Bhadra +3 more
semanticscholar +6 more sources
Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction [PDF]
HeliyonBackground: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction.
Varin Jaruvongvanich +10 more
doaj +2 more sources
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [PDF]
Computer Vision and Pattern Recognition, 2021 Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system.
Yuanhao Cai +7 more
semanticscholar +1 more source
RealFusion 360° Reconstruction of Any Object from a Single Image [PDF]
Computer Vision and Pattern Recognition, 2023 We consider the problem of reconstructing a full 360° photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed.
Luke Melas-Kyriazi +3 more
semanticscholar +1 more source
Review of Super-Resolution Image Reconstruction Algorithms [PDF]
Jisuanji kexue yu tansuo, 2022 In human visual perception system, high-resolution (HR) image is an important medium to clearly express its spatial structure, detailed features, edge texture and other information, and it has a very wide range of practical value in medicine, criminal ...
ZHONG Mengyuan, JIANG Lin
doaj +1 more source
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022 We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.
Yutong Xie, Quanzheng Li
semanticscholar +1 more source
Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
doaj +1 more source
NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses additional challenges due to limited measurements. In this work, we propose a methodology of
Liyue Shen, J. Pauly, Lei Xing
semanticscholar +1 more source