A block-based noise level estimation from X-ray images in SVD domain

Turajlić, Emir; Skaljo, Namir; Begović, Alen

doi:10.1109/iwssip.2017.7965569

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…From Eq (2), we can obtain different Level-of-Detail (LOD) data when considering different numbers of eigenvalues and the related eigenvectors. Furthermore, some eigenvalues are close to zero, which means that their effect in reconstructing matrix B is minor [17]. Then, these eigenvalues and the related eigenvectors can be ignored.…”

Section: Plos Onementioning

confidence: 99%

A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model simplification

Wu¹,

Li²,

Zhang³

et al. 2020

PLoS ONE

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The proposed method is to do simplification for Digital Elevation Model (DEM), which uses a few of original nodes representing the terrain surface while maintaining the accuracy. The original DEM nodes are sampled using the Maximal Poisson-disk Sampling (MPS), in which, the disk’s size of each sample is computed on basis of the Singular Value Decomposition (SVD). MPS can generate the hyper-uniformly distributed samples and was taken to do DEM adaptive sampling by being combined with the geodesic metric. However, the geodesic distance computation is complex and the requirement for memory is high. As such, this paper proposes an extension of the classic MPS based method for selecting quasi-randomly distributed points from DEM nodes based on the distribution of eigenvalues, accounting for surface heterogeneity. To achieve this objective, uniform MPS is conducted to sample the DEM nodes by setting the related disk radius to be inversely proportional to the local terrain complexity, which is defined as an index expressing the local terrain variation. Then, the geodesic metric related parameters are implicitly contained in the defined index. As a result, more samples are concentrated in the rugged regions, and vice versa. The proposed method shows better perfermance, at least the results are comparable with the geodesic distance based Poisson disk sampling method. Meanwhile, it greatly accelerates the sampling process and reduces the memory cost.

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Section: Plos Onementioning

confidence: 99%

A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model simplification

Wu¹,

Li²,

Zhang³

et al. 2020

PLoS ONE

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Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images

Hariharan¹,

Strobel²,

Kaethner³

et al. 2020

Phys. Med. Biol.

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Purpose. Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. Materials and Methods. We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. Results. Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. Conclusion. The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.

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A block-based noise level estimation from X-ray images in SVD domain

Cited by 2 publications

References 14 publications

A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model simplification

A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model simplification

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images

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