Robust Steganography based on Matching Pixel Locations

Halidou, Aminou; Mohamadou, Youssoufa; Olle, O Georges Delort

doi:10.5120/ijca2017914252

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…It is constructed based on the intensity on the same wavelength. A lowpass Butterworth filter, designed to suppress high frequencies, is applied [33] . The Butterworth filter can be written as Equation (2).…”

Section: Hyperspectral Image Denoisingmentioning

confidence: 99%

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Lin,

Chen,

Xie

et al. 2023

Preprint

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An optimal margin distance determination is crucial for successful resection surgeries targeting early-stage non-small cell lung cancer (NSCLC). Yet, there has been no clear evidence regarding optimal margin distance, which is tightly associated with the recurrence and mortality after the operation. In this study, we proposed an innovative approach, a laser-induced breakdown hyperspectral imaging (LIBS-HSI) technique, capable of capturing the multi-element distribution within tumour tissues to probe optimal margin distance. The imaging quality of LIBS-HSI was enhanced by hyperspectral image (HSI) denoising algorithms founded on sparse representation and low-rank constraint, complemented by the application of the Butterworth notch resist filter. A semi-supervised deep learning model was developed to achieve pathological imaging segmentation recognition of malignant areas. This technique revealed the margin distance after getting malignant areas prediction and gradient, based on pseudo-colour imaging of elements composition within NSCLC samples. It stands as a promising candidate in postoperative pathological reports, enabling doctors to evaluate surgical outcomes in the future effectively.

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Section: Hyperspectral Image Denoisingmentioning

confidence: 99%

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Lin,

Chen,

Xie

et al. 2023

Preprint

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“…After improvements based on the EMD algorithm, integrated empirical mode decomposition (EEMD) [3] and fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) can be obtained [4] , several algorithms of fully adaptive noise ensemble empirical mode decomposition ICEEMDAN [5] . The wavelet threshold denoising algorithm can achieve the purpose of denoising by distinguishing the amplitude-frequency characteristics of signals and noise [6] . The normalized least mean square (NLMS) algorithm [7] is optimized based on the step size changing with time and is optimized on the basis of the least mean square (LMS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

yin,

shi

2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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The settlement deformation of subway tunnels during the construction and operation stages is a relatively complex nonlinear process. The original settlement deformation monitoring data will be affected by some noise during the collection process. In order to reduce the impact of noise and errors in the subsequent prediction process, a joint denoising model based on the improved empirical mode decomposition (ICEEMDAN) algorithm, wavelet threshold denoising and NLMS adaptive filtering was constructed. The model first uses ICEEMDAN to decompose the original data. After decomposing the intrinsic modal component IMF, it divides it into high-frequency and low-frequency components. Then the wavelet threshold is used to remove the components with high correlation coefficients in the high-frequency and low-frequency components. Noise and NLMS adaptive filtering are processed, and finally the additive reconstruction is performed to obtain the denoised data. Experimental analysis results show that compared with ICEEMDAN, wavelet threshold denoising and NLMS adaptive filtering algorithms, the joint denoising model has better denoising effect, higher correlation coefficient, signal-to-noise ratio and smaller mean square root error.

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A new high capacity and secure image realization steganography based on ASCII code matching

Elshoush

Mahmoud

Altigani

2021

Multimed Tools Appl

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Robust Steganography based on Matching Pixel Locations

Cited by 3 publications

References 17 publications

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

A new high capacity and secure image realization steganography based on ASCII code matching

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