Image denoising based on gaussian/bilateral filter and its method noise thresholding

Kumar, B. Ganesh

doi:10.1007/s11760-012-0372-7

Cited by 104 publications

(26 citation statements)

References 24 publications

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“…Portilla et al [16] Threshold method via Bayesian wavelet coring Simoncelli and Adelson [21] Bayes approach using Jeffrey's noninformative prior Figueiredo and Nowak [11] Threshold method with Bivariate shrinkage function Sendur and Selesnick [18] Threshold method via bilateral filter Shreyamsha Kumar [19] Threshold method via nonlocal means filter Shreyamsha Kumar [20] to the shrinkage of large coefficients. The hard thresholding estimates tend to have bigger variance and can be unstable so as to yield visual distortions, e.g., pseudo-Gibbs.…”

Section: Methods Referencesmentioning

confidence: 99%

“…Even though image denoising, especially Bayesian methods have long been a focus of research, there always remains room for improvement, because noise suppression/reduction is a delicate and a difficult task due to the fact that there is a tradeoff between noise reduction and preservation of actual image features. Therefore, some alternatives [19,20] emerge for the image denoising, such as bilateral filter method. Bilateral Filter [19] considers both spatial and intensity information between a point and its neighboring points, unlike the conventional linear filtering where only spatial information is considered.…”

Section: Methods Referencesmentioning

confidence: 99%

“…Therefore, some alternatives [19,20] emerge for the image denoising, such as bilateral filter method. Bilateral Filter [19] considers both spatial and intensity information between a point and its neighboring points, unlike the conventional linear filtering where only spatial information is considered. This preserves the edges/sharp boundaries very well while noise is averaged out as it average pixels belonging to the same region as the reference pixel.…”

Section: Methods Referencesmentioning

confidence: 99%

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A novel curvelet thresholding denoising method based on chi-squared distribution

Cui

Yan

Song

2014

SIViP

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In this paper, we first explored the advantages and disadvantages of the classical curvelet thresholding functions. To circumvent the inherent drawbacks of them, we introduced the chi-squared cumulative distribution to develop a novel thresholding function. The theoretical analysis and mathematical proofs show that the proposed thresholding function represents a compromise between soft and hard thresholding function and offers potential advantages of generally less sensitivity to small perturbations in the data over hard thresholding function and smaller bias and overall reconstruction error over soft thresholding function. Additionally, the proposed function is comparable to the semisoft thresholding function, while the latter requires two thresholds. Moreover, our proposed function can adjust flexibly between the hard and soft thresholding function through the parameter n, providing a better balance between noise removal and detail preservation. Numerical experiments also demonstrate that the proposed thresholding function outperforms the three classical ones in terms of PSNR and visual quality.

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Section: Methods Referencesmentioning

confidence: 99%

Section: Methods Referencesmentioning

confidence: 99%

Section: Methods Referencesmentioning

confidence: 99%

See 1 more Smart Citation

A novel curvelet thresholding denoising method based on chi-squared distribution

Cui

Yan

Song

2014

SIViP

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“…Since only local information of image pixels cannot achieve high performance, a nonlocal mean (NLM) filter [14] is proposed to address this issue. By utilizing a concept of nonlocal method, similar image patches are taken into account in order to perform noise removing using weight average algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In [6,13], the fuzzy rule is utilized to formulate weight averaging where the weight of noisiness and weight of similarity between the considered pixel and neighbors were taken into account. Nonlocal mean (NLM) approach [14] is exploited. By utilizing NLR; not only are neighbors considered, but patches which contain similar details are also determined.…”

Section: Introductionmentioning

confidence: 99%

Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

Langampol

Srisomboon

Patanavijit

et al. 2019

Mathematical Problems in Engineering

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Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

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Deep learning based automated detection of intraretinal cystoid fluid

Ahmed

Panhwar

Baqai

et al. 2021

Int J Imaging Syst Tech

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The diversified ocular disorders in which cystoid macular edema (CME) occurs, are strongly associated with the vision loss. Optical coherence tomography (OCT) scans that allow screening of the retina, contain artifacts including blur‐edges, speckle noise, and so forth, which create difficulty in identifying retinal fluid. In this work, major image preprocessing techniques such as minimum filtering, block‐matching and 3D filtering, and Richardson–Lucy deconvolution method are applied to minimize noise and other degradation effects from OCT scans, ensured by maintaining image quality assessment scores, that is, blind‐less image spatial quality evaluator and sharpness estimation score. Furthermore, this work develops an automated method based on deep learning (DL) to detect the presence and progression of retinal fluid, that is, CME and its quantification. Specifically, U‐net model with efficient tuning of hyperparameters is proposed to obtain optimal results. The algorithm's performance is evaluated for accurate identification of fluid localization in the case of diabetic macular edema (DME), having CME regions, and compared against the manual segmentation of fluid by experts, which are highly correlated. This enhanced DL method achieves better performance than the algorithms previously reported. The proposed algorithm is evaluated on several performance metrics during training and reports the best score of 99.81, 0.50, 83.34, 86.56, 80.89, and 99.48% for model accuracy, binary cross‐entropy loss, Dice coefficient (DC), precision, recall, and area under the ROC‐curve, respectively. Additionally, fourfold cross‐validation technique is also applied and an average DC score of 84.00% is obtained. This approach can be helpful to prevent vision loss.

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Image denoising based on gaussian/bilateral filter and its method noise thresholding

Cited by 104 publications

References 24 publications

A novel curvelet thresholding denoising method based on chi-squared distribution

A novel curvelet thresholding denoising method based on chi-squared distribution

Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

Deep learning based automated detection of intraretinal cystoid fluid

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