Results 141 to 150 of about 302,463 (187)
Myxofibrosarcomas Have Extremely Low Internal Echoes: A Case Report. [PDF]
CureusTamori T, Oura S.
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Artificial intelligence-assisted retinal imaging enables dense pixel sampling from sparse measurements. [PDF]
NPJ Artif IntellDas V +4 more
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Computed tomography in pediatric blunt abdominal trauma: current evidence, challenges, and future directions - a systematic review and meta-analysis. [PDF]
Scand J Trauma Resusc Emerg MedAlsabri M +6 more
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Optical sparse aperture imaging
Applied Optics, 2007The resolution of a conventional diffraction-limited imaging system is proportional to its pupil diameter. A primary goal of sparse aperture imaging is to enhance resolution while minimizing the total light collection area; the latter being desirable, in part, because of the cost of large, monolithic apertures.
Miller, Nicholas J. +2 more
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Research in Optical Sciences, 2014
We use heralded-single photons and a gated camera to image biological samples. The low photon number means the images are inherently subject to Poissonian noise that we mitigate using compressed sensing techniques.
Reuben Aspden +4 more
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We use heralded-single photons and a gated camera to image biological samples. The low photon number means the images are inherently subject to Poissonian noise that we mitigate using compressed sensing techniques.
Reuben Aspden +4 more
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Image Super-Resolution Via Sparse Representation
IEEE Transactions on Image Processing, 2010This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary.
Yang, Jianchao +3 more
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Structured Sparse Priors for Image Classification
IEEE Transactions on Image Processing, 2013Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization).
Umamahesh, Srinivas +4 more
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2011 18th IEEE International Conference on Image Processing, 2011
In this paper, we propose a novel method for denoising images corrupted by the mixture of the additive white Gaussian noise and the heavy tailed noise. The proposed method is based on robust statistical approach, i.e. application of M-estimators in the combination with l 1 sparse regularization technique.
Radovan Obradovic +4 more
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In this paper, we propose a novel method for denoising images corrupted by the mixture of the additive white Gaussian noise and the heavy tailed noise. The proposed method is based on robust statistical approach, i.e. application of M-estimators in the combination with l 1 sparse regularization technique.
Radovan Obradovic +4 more
openaire +1 more source