Bayesian image denoising using two complementary discontinuity measures

Jung, Cheolkon; Jiao, Licheng; Gong, Maoguo

doi:10.1049/iet-ipr.2010.0057

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High pass filter is used to obtain image information at different scales. On the other hand, the reseachers [5] introduced a new Bayesian image de-noising technique with two complementary discontinuity measures. The spatial-gradient, and the other which is a continuity measure detects contextual discontinuities for feature preservation as shown in his findings whereby a clear high peak signal to noise ratio (PSNR) is gained from noisy images, and the noise is successfully decreased while preserving edge components.…”

Section: Introductionmentioning

confidence: 99%

Image denoising using wavelet thresholding and median filter based Raspberry pi

2021

View full text Add to dashboard Cite

The goal of any de-noising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner otherwise artefacts can be introduced which may blur the image. In this work, three levels of Gaussian noise are used for adding noise on the original image (σ=10, σ=50, σ=100) and also (σ=15, σ=20, σ=25) to compare with Ramadhan et al.[1] and analysis with it to test embedded system with median filter. Performance evaluation of the median filter, wavelet threshold de-noising techniques is provided. The techniques used are namely the median filter and wavelet threshold is used to remove noise based on raspberry pi with Python. Four methods to remove noise image are used. MF, WT, MF before and after WT. The results showed the image of camera was better than other after tested all the methods with Gaussian noise σ=10. On other hand the other images were better than image of camera for the Gaussian level 50 and 100. The results were good in median filter in wavelet threshold based on Raspberry Pi, which is compared with overall result most of images butter in median filter. KEYWORDSImage Denoising Median Filter Wavelet Threshold Gaussian Noise This is an open-access article under the CC-BY-SA license This research devided in severap part. Section II explain the m methodology includes Literature Review, addition noise model, spatial domain filtering, Wavelet transform, Wavelet threshold, and the parameters. The results willo be delivered in Section III, includes Image Denoising, comparison benchmark. The last section (IV) shows the conclusion.

show abstract

Section: Introductionmentioning

confidence: 99%

Image denoising using wavelet thresholding and median filter based Raspberry pi

2021

View full text Add to dashboard Cite

show abstract

“…These derivatives are used to formulate the rules which differentiate the noisy and the pixels on the edge or flat region and preserve the image details. The fuzzy methods for image denoising are, using the fuzzy sets and fuzzy logic [8,9,10,11,12,13] neuro-fuzzy systems [14], decision based variational techniques [15], cellular automat techniques [16], and bayesian techniques for image denoising [17] and evidence theory [18]. Commonly fuzzy logical based methods use the median, adaptive median and mean filters and median filters in two stages.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule Based Salt and Pepper Noise Removing in Gray Images

Salamat¹,

Missen²

2015

BJMCS

View full text Add to dashboard Cite

This paper presents the application of the diffusion equation for removing the salt & pepper noise in the field of image processing along with the fuzzy logic. The fuzzy logic differentiates the noisy pixels, especially the salt & pepper noise and the edges, then diffusion equation is applied to only noisy pixels and the diffusivity is proportional to the noise. The proposed method is applied to many test images. The simulated results demonstrate the effectiveness of the method based on statistical measures such as Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM).

show abstract

Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm

Kittisuwan

2020

Int. J. Artif. Intell. Tools

View full text Add to dashboard Cite

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal corrupted by additive white Gaussian noise (AWGN) is considered. Therefore, this paper proposes new shrinkage functions for non-convex penalty functions, modified arctangent and exponential models, in fused lasso formulation. A lot of works present the shrinkage function for arctangent penalty function. Unfortunately, there is no closed-form solution. The numerical solution is required for shrinkage function of this penalty function. However, the analytical solution is derived in this paper. Moreover, the shrinkage function of modified exponential penalty function is proposed. This shrinkage function obtains from simple iterative method, fixed-point algorithm. We demonstrate the proposed methods through simulations with standard one-dimensional signals contaminated by AWGN. The proposed techniques are compared with traditional estimation methods, such as total variation (TV) and wavelet denoising methods. In experimental results, our proposed methods outperform several exiting methods both visual quality and in terms of root mean square error (RMSE). In fact, the proposed methods can better preserve the feature of noise-free signal than the compared methods. The denoised signals produced by the proposed methods are less smooth than the denoised signals produced by the compared methods.

show abstract

Bayesian image denoising using two complementary discontinuity measures

Cited by 4 publications

References 29 publications

Image denoising using wavelet thresholding and median filter based Raspberry pi

Image denoising using wavelet thresholding and median filter based Raspberry pi

Fuzzy Rule Based Salt and Pepper Noise Removing in Gray Images

Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm

Contact Info

Product

Resources

About