Additive and Multiplicative Noise Removal Based on Adaptive Wavelet Transformation Using Cycle Spinning

Khmag,

doi:10.3844/ajassp.2014.316.328

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…(5) depicted the covariance matrix of contaminated sub-image represents the covariance matrix of the original sub-image (patches), and represents the lowest eigenvalue of matrix . The analysis of the lowest eigenvalue of the covariance matrix of the noisy patches is represented in (5). Furthermore, the model of AWGN is found accordingly.…”

Section: Research Estimation Of Noise Levels Using Pcamentioning

confidence: 99%

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Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

et al. 2018

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In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is b uilt according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-b ased estimation technique in order to estimate the noise level in natural images and use it in b lind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub -pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the b est performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.

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Section: Research Estimation Of Noise Levels Using Pcamentioning

confidence: 99%

“…Theoretically, quaternion wavelet transform is used widely in the field of image noise removal [4]- [5], classification of deep details [6]- [7], deblurring and its applications [8] and computer fusion [9]. In [10], a novel generalized signal-dependent for noise model is designed in natural images which acquired using a digital camera.…”

Section: Introductionmentioning

confidence: 99%

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

et al. 2018

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“…By computing each posterior mean and posterior variance of parametric component parameters and smoothing spline functions, confidence intervals can be constructed for the parametric component parameters and confidence interval smoothing spline functions for nonparametric components in semiparametric additive regression models. Khmag et al (2014) Exposes that a newly developed method based on the wavelet transform (semi-soft thresholding) there is a practical guidance on its use. Cycle Spinning technique is implemented in order to enhance the quality of the denoised estimates.…”

Section: Jcsmentioning

confidence: 99%

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

Rajathi¹,

Rangarajan²,

R³

et al. 2014

Journal of Computer Science

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The image data is normally corrupted by additive noise during acquisition. This reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to restore the original image. In proposed method a wavelet shrinkage algorithm based on fuzzy logic and the DT-DWT scheme is used. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This model differentiates the important coefficients and the coefficients belong to image discontinuity and noisy coefficients. This fuzzy model is used to enhance the wavelet coefficients' information in the shrinkage step which uses the fuzzy membership function to shrink wavelet coefficients based on the fuzzy feature. The effectiveness of image denoising depends upon the estimation of noise variance of noisy image, the noise variance is estimated using smoothing spline Estimation. This study examine image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Simulation result shows our approach achieves a substantial improvement in both PSNR and Visual quality.

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A Robust Watermarking Technique for High-Efficiency Video Coding (HEVC) Based on Blind Extraction Scheme

Khmag

2021

SN COMPUT. SCI.

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In the field of multimedia, real-time video encoding systems and copyright protections, digital watermarking plays main role and shows promising means. In the same regard, digital watermarking is applied in compressed domain for video transmitting and storing to protect the contents of the main video signal. The need for video compressing and embedding has proven its importance due to its wide applications in daily life activities to protect these videos to be easily manipulated (e.g., trimmed, cropped, re-compressed). There have been several compression techniques for digital videos. One of the efficient standard compression video techniques is High-Efficiency Video Coding (HEVC), a beneficiary to H.264 Advanced Video Coding (AVC), where it is considered as a latest standard for video compression with high compression rate. In this study, a robust digital watermarking technique with a blind extraction procedure for HEVC-encoded video is proposed. From practical point of view, a readable digital watermark is embedded imperceptibly in 4 × 4 intra-predicted sub-blocks of the HEVC-tested video. The proposed watermarking structure imposes security by monitoring the spatio-temporal features in the target compressed video and generate a random key to select the appropriate embedding regions. The HEVC standard encoded video was exploiting to implement the embedding procedure. Practically, the experimental outcomes show that the proposed work restricts the increase in video bit rate and degradation of perceptual quality. In addition, the proposed scheme produces an average performance of the visual degradation up to 0.42 dB at the expense of 2.85% bit rate overhead and it shows good quality and robustness against additive Gaussian noise.

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Additive and Multiplicative Noise Removal Based on Adaptive Wavelet Transformation Using Cycle Spinning

Cited by 8 publications

References 26 publications

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

A Robust Watermarking Technique for High-Efficiency Video Coding (HEVC) Based on Blind Extraction Scheme

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