arXiv: Image and Video Processing
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Papers 4,191
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#1Xianping Li (ASU: Arizona State University)H-Index: 8
#2Teresa Wu (ASU: Arizona State University)
Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR wor...
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#1Yao SunH-Index: 41
#2Lichao MouH-Index: 26
Last. Xiao Xiang ZhuH-Index: 53
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Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image. Based on the radar viewing geometry, we propose that this problem can be formulated as a bounding box regression problem and therefore allows for integrating height data from multipl...
#1Hao Xu ('KCL': King's College London)H-Index: 3
#2Steven A. Niederer ('KCL': King's College London)H-Index: 35
Last. Alistair A. Young ('KCL': King's College London)H-Index: 58
view all 6 authors...
Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be le...
#1George Yiasemis (NKI-AVL: Netherlands Cancer Institute)
#2Clara I. Sánchez (UvA: University of Amsterdam)H-Index: 35
Last. Jonas Teuwen (NKI-AVL: Netherlands Cancer Institute)H-Index: 3
view all 4 authors...
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours. However, MRI suffers from very long acquisition times that make it susceptible to patient motion artifacts and limit its potential to deliver dynamic treatments. Conventional approaches such as Parallel Imaging and Compressed Sensing allow for an increase in MRI acquisition speed by reconstructing MR images by acqui...
#1Farhanaz FarheenH-Index: 2
#2M. S. ShamilH-Index: 1
Last. M. Sohel Rahman (BUET: Bangladesh University of Engineering and Technology)H-Index: 24
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Lung cancer is a leading cause of death in most countries of the world. Since prompt diagnosis of tumors can allow oncologists to discern their nature, type and the mode of treatment, tumor detection and segmentation from CT Scan images is a crucial field of study worldwide. This paper approaches lung tumor segmentation by applying two-dimensional discrete wavelet transform (DWT) on the LOTUS dataset for more meticulous texture analysis whilst integrating information from neighboring CT slices b...
#1Simon Grosche (FAU: University of Erlangen-Nuremberg)H-Index: 3
#2Andy Regensky (FAU: University of Erlangen-Nuremberg)H-Index: 1
Last. Andre Kaup (FAU: University of Erlangen-Nuremberg)H-Index: 27
view all 4 authors...
For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. In doing so, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of usin...
#1Zhishen Huang (MSU: Michigan State University)H-Index: 3
#2Saiprasad Ravishankar (MSU: Michigan State University)H-Index: 24
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements. Often sophisticated reconstruction algorithms are deployed to maintain high image quality in such settings. In this work, we propose a data-driven sampler using a convolutional neural network, MNet, to provide object-specific sampling patterns adaptive to each scanned object. The network observes very limited low-frequency k-space data for each object and rapidly predi...
#1Xi ChengH-Index: 9
Last. Jian Yang (Nanjing University of Science and Technology)H-Index: 142
view all 5 authors...
Structured illumination microscopy (SIM) is an important super-resolution based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low light and short exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this paper, we propose a sing...
We consider using the system's optical imaging process with convolutional neural networks (CNNs) to solve the snapshot hyperspectral imaging reconstruction problem, which uses a dual-camera system to capture the three-dimensional hyperspectral images (HSIs) in a compressed way. Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image an...
#1Heming SunH-Index: 12
#2Lu YuH-Index: 22
Last. Jiro KattoH-Index: 3
view all 3 authors...
End-to-end Learned image compression (LIC) has reached the traditional hand-crafted methods such as BPG (HEVC intra) in terms of the coding gain. However, the large network size prohibits the usage of LIC on resource-limited embedded systems. This paper reduces the network complexity by quantizing both weights and activations. 1) For the weight quantization, we study different kinds of grouping and quantization scheme at first. A channel-wise non-linear quantization scheme is determined based on...
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