Signal separation from X-ray image sequence using singular value decomposition

Yu, Chunyu; Sun, Jingyang

doi:10.1016/j.bspc.2018.01.012

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Various methods can be utilized to reduce the noise of spectral data and enhance the spectral characteristics. Singular value decomposition (SVD) can eliminate most of the noise in the image by assuming a linear relationship between the noise and the spectral information [29]. Since the Fourier transform (FT) has the ability to capture the nonstationary characteristics of real signals, this method is utilized to reduce the noise in the image [30].…”

Section: Introductionmentioning

confidence: 99%

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Meng

Bao

et al. 2021

Remote Sensing

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In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.

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Section: Introductionmentioning

confidence: 99%

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Meng

Bao

et al. 2021

Remote Sensing

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“…The XCA image is a display of the x-ray attenuation sum along x-ray projection paths, and it contains various overlapped anatomical structures besides the contrast-filled vessels, including bones, diaphragms, and lungs. Furthermore, XCA images from low-dose x-ray imaging are seriously corrupted by spatially varying signal-dependent Poisson noises (Yu and Sun 2018, Zhu et al 2013, such that the XCA image has very low contrast and low SNR between the noises and the signals. Therefore, segmenting the contrast-filled 2D+t vessels from the noisy and complex backgrounds in an XCA image sequence is a challenging open problem in biomedical imaging.…”

Section: Introductionmentioning

confidence: 99%

Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels

et al. 2018

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This letter proposes to extract contrast-filled vessels from overlapped noisy complex backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse decomposition. A refined vessel segmentation is finally achieved by implementing a radon-like feature filtering plus local-to-global adaptive thresholding to tackle the spatially varying noisy residuals in the extracted vessels. Based on real and synthetic XCA data, the experiment results demonstrate the superiority of the proposed method over the state-of-the-art methods.

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Signal separation from X-ray image sequence using singular value decomposition

Cited by 2 publications

References 22 publications

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels

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