Dataset Information

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing.

ABSTRACT: Pan-sharpening aims at integrating spectral information from a multi-spectral (MS) image and spatial information from a panchromatic (PAN) image in a fused image with both high spectral and spatial resolutions. Numerous pan-sharpening methods are based on intensity-hue-saturation (IHS) transform, which may cause evident spectral distortion. To address this problem, an IHS-based pan-sharpening method using ripplet transform and compressed sensing is proposed. Firstly, the IHS transform is applied to the MS image to separate intensity components. Secondly, discrete ripplet transform (DRT) is implemented on the intensity component and the PAN image to obtain multi-scale sub-images. High-frequency sub-images are fused by a local variance algorithm and, for low-frequency sub-images, compressed sensing is introduced for the reconstruction of the intensity component so as to integrate the local information from both the intensity component and the PAN image. The specific fusion rule is defined by local difference. Finally, the inverse ripplet transform and inverse IHS transform are coupled to generate the pan-sharpened image. The proposed method is compared with five state-of-the-art pan-sharpening methods and also the Gram-Schmidt (GS) method through visual and quantitative analysis of WorldView-2, Pleiades and Triplesat datasets. The experimental results reveal that the proposed method achieves relatively higher spatial resolution and more desirable spectral fidelity.

SUBMITTER: Yang C

PROVIDER: S-EPMC6263882 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing.

Yang Chen C Zhan Qingming Q Liu Huimin H Ma Ruiqi R

Sensors (Basel, Switzerland) 20181025 11

Pan-sharpening aims at integrating spectral information from a multi-spectral (MS) image and spatial information from a panchromatic (PAN) image in a fused image with both high spectral and spatial resolutions. Numerous pan-sharpening methods are based on intensity-hue-saturation (IHS) transform, which may cause evident spectral distortion. To address this problem, an IHS-based pan-sharpening method using ripplet transform and compressed sensing is proposed. Firstly, the IHS transform is applied ...[more]

PMID: 30366414

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Project description:BackgroundT-wave alternans (TWA) has been associated with increased vulnerability to ventricular tachyarrhythmias and sudden cardiac death. However, both random (white) noise and (patho)physiologic processes (ie, premature ventricular contractions and heart and respiration rates) may hamper TWA estimation and therefore lessen its clinical utility for risk stratification.ObjectiveTo investigate the effect of random noise and certain (patho)physiologic processes on the estimation of TWA by using the fast Fourier transform method and to develop methods to overcome these potential sources of error.MethodsWe used a combination of human electrocardiogram data and computer simulations to assess the effects of a premature ventricular contraction and random and colored noise on the accuracy of TWA estimation.ResultsWe quantitatively demonstrate that replacing a "bad" beat with an odd/even median beat is a more accurate approach than replacing it with the overall average or the overall median beat. We also show that phase resetting may have a significant effect on alternans estimation and that estimation of alternans by using frequencies >0.4922 cycles/beat in a 128-point fast Fourier transform provides the most accurate approach for estimating the alternans when phase resetting is likely to occur. In addition, our data demonstrate that the number of indeterminate TWA tests due to high levels of noise can be reduced when the alternans voltage exceeds a new higher threshold. Furthermore, the amplitude of random noise has a significant effect on alternans estimation and the alternans voltage threshold should be adjusted for noise levels >1.8 μV. Finally, we quantitatively demonstrate that colored noise may lead to a false-positive or a false-negative result. We propose methods to estimate the effect of these (patho)physiologic processes on the alternans estimation in order to determine whether a TWA test is likely to be a true positive or a true negative.ConclusionThis study introduces novel methods to overcome potential sources of error in the estimation of TWA. These methods may improve the utility of TWA either for ambulatory monitoring or for clinical risk stratification for ventricular arrhythmias and sudden cardiac death.

Project description:While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct the EAP from significantly undersampled q-space data. We present a post mortem validation study where we evaluate the ability of CS-DSI to approximate not only fully sampled DSI but also multi-shell acquisitions with high fidelity. Human brain samples are imaged with high-resolution DSI at 9.4T and with polarization-sensitive optical coherence tomography (PSOCT). The latter provides direct measurements of axonal orientations at microscopic resolutions, allowing us to evaluate the mesoscopic orientation estimates obtained from diffusion MRI, in terms of their angular error and the presence of spurious peaks. We test two fast, dictionary-based, L2-regularized algorithms for CS-DSI reconstruction. We find that, for a CS acceleration factor of R=3, i.e., an acquisition with 171 gradient directions, one of these methods is able to achieve both low angular error and low number of spurious peaks. With a scan length similar to that of high angular resolution multi-shell acquisition schemes, this CS-DSI approach is able to approximate both fully sampled DSI and multi-shell data with high accuracy. Thus it is suitable for orientation reconstruction and microstructural modeling techniques that require either grid- or shell-based acquisitions. We find that the signal-to-noise ratio (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of CS-DSI, but that there is substantial robustness to loss of SNR in the test data. Finally, we show that, as the CS acceleration factor increases beyond R=3, the accuracy of these reconstruction methods degrade, either in terms of the angular error, or in terms of the number of spurious peaks. Our results provide useful benchmarks for the future development of even more efficient q-space acceleration techniques.

Dataset Information

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing.

Publications

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing.

Similar Datasets

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