An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising

Feng, Jianzhou; Song, Li; Huo, Xiaoming; Yang, Xiaokang; Zhang, Wenjun

doi:10.1109/lsp.2014.2350032

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Reference [93] proposed the patch group deep learning for image denoising. A training set with a patch group was created and then the deep learning method [94,95] was used to reduce the noise. Reference [96] developed an end-to-end deep neural network (DDANet) for computational ghost image reconstruction.…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

168

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Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

168

View full text Add to dashboard Cite

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“…Of course, (12) will not provide a good estimate of λ * for other parameter settings. One option is to maintain a lookup table of the optimal rules for selected 6 parameter settings, and then interpolate between them. Instead, we propose to use (12) to initialize a one-dimensional search, irrespective of the parameter setting.…”

Section: A Parametersmentioning

confidence: 99%

“…For example, various weighting schemes for the center pixel have been proposed in [3], [4], [5]. A weighting scheme based on quadratic programming was proposed in [6], which exploits the overlapping information between adjacent patches. Further, optimization framework based approaches have been proposed in [7], [8] to improve the performance of NLM.…”

Section: Introductionmentioning

confidence: 99%

Pruned non‐local means

Ghosh¹,

Mandal²,

Chaudhury³

2017

IET Image Processing

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In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches. We demonstrate that the denoising performance of NLM can be improved by pruning the neighboring pixels, namely, by rejecting neighboring pixels whose weights are below a certain threshold λ. While pruning can potentially reduce pixel averaging in uniform-intensity regions, we demonstrate that there is generally an overall improvement in the denoising performance. In particular, the improvement comes from pixels situated close to edges and corners. The success of the proposed method strongly depends on the choice of the global threshold λ, which in turn depends on the noise level and the image characteristics. We show how Stein's unbiased estimator of the mean-squared error can be used to optimally tune λ, at a marginal computational overhead. We present some representative denoising results to demonstrate the superior performance of the proposed method over NLM and its variants.

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“…In this study, the weighted coefficient in the mathematical model above can be optimized by PSO, which regards a particle as an available solution [16], then guides the particle to its optimal position using itself and its neighboring particles' information.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Extraction for fetal ECG using single channel blind source separation algorithm based on multi-algorithm fusion

Chen

Liang

et al. 2016

MATEC Web of Conferences

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Nowadays, detecting fetal ECG using abdominal signal is a commonly used method, but fetal ECG signal will be affected by maternal ECG. Current FECG extraction algorithms are mainly aiming at multiple channels signal. They often assume there is only one fetus and did not consider multiple births. This paper proposed a single channel blind source separation (SCBSS) algorithm based on source number estimation using multi-algorithm fusion to process single abdominal signal. The method decomposed collected single channel signal into multiple intrinsic mode function (IMF) utilizing Empirical Mode Decomposition (EMD), mapping single channel into multiple channels. Four multiple channel source number estimation (MCSNE) methods (Bootstrap, Hough, AIC and PCA) were weighting fused to estimate accurate source number and the particle swarm optimization algorithm (PSO) was employed to determine weighted coefficient. According to source number and IMF, nonnegative matrix was constructed and nonnegative matrix factorization (NMF) was employed to separate mixed signals. Experiments used single channel signal mixed by four man-made signals and single channel ECG mixed by two to verify the proposed algorithm. Results showed that the proposed algorithm could determine number of independent signal in single acquired signal. FECG could be extracted from single channel observed signal and the algorithm can be used to solve separation of MECG and FECG.

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An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising

Cited by 13 publications

References 8 publications

Methods for image denoising using convolutional neural network: a review

Methods for image denoising using convolutional neural network: a review

Pruned non‐local means

Extraction for fetal ECG using single channel blind source separation algorithm based on multi-algorithm fusion

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