A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

Kaisserli, Zineb; Laleg‐Kirati, Taous‐Meriem; Lahmar-Benbernou, Amina

doi:10.1016/j.dsp.2015.01.005

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…An efficient numerical algorithm has been proposed in [14] to reconstruct images using the SCSA. The idea consists in splitting the 2D Schrödinger operator H 2,h (I ) into two unidimensional operators and in solving the eigenvalues problem row by row and column by column.…”

Section: Scsa Denoising Methodsmentioning

confidence: 99%

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A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

Chahid

Serrai

Achten

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this paper, a new Region Of Interest (ROI) characterization for Magnetic Resonance Imaging (MRI) is proposed for denoising performance evaluation without noise estimation. This technique consists of balancing the contrast between the dark and bright ROIs to track the noise removal and achieve an optimal compromise between removing noise and preserving image details. The ROI technique has been tested using synthetic MRI images. It has been applied to a new denoising method called Semi-Classical Signal Analysis (SCSA).The SCSA decomposes the image into the squared eigenfunctions of the Schrödinger operator where a soft threshold h is used to remove the noise from the noisy image. The obtained results using real MRI data prove that this method is suitable for real medical image processing where the noise-free image is not available.

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Section: Scsa Denoising Methodsmentioning

confidence: 99%

“…This metric is more suitable for iterative methods that don't estimate or use a predefined model for the noise. Therefore, The new metric has been used with a recent method so-called Semi-Classical Signal Analysis (SCSA) [14]. The later presents a new concept of image denoising based on the Schrödinger operator.…”

Section: Introductionmentioning

confidence: 99%

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

Chahid

Serrai

Achten

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…An efficient numerical implementation of this algorithm has been proposed for images reconstruction in [8] . The idea consists in splitting the 2D Schrödinger operator into two one dimensional operators [12] and in solving the eigenvalues problem row by row and column by column.…”

Section: Standard Scsa-fixed H Denoising Methodsmentioning

confidence: 99%

“…The squared eigenfunctions of these one dimensional operators are then combined using a tensor product approach. It is known from [8] that as k increase as the eigenfunctions presents more oscillation. Therefore represent the noise.…”

Section: Standard Scsa-fixed H Denoising Methodsmentioning

confidence: 99%

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Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator

Chahid

Serrai

Achten

et al. 2017

2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.Index Terms-Magnetic Resonance Imaging (MRI), adaptive image denoising, Semi-Classical Signal Analysis (SCSA), eigenfunctions of the Schrödinger operator.

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A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

et al. 2022

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A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

Cited by 15 publications

References 16 publications

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator

A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

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