Explicit Coherence Enhancing Filter With Spatial Adaptive Elliptical Kernel

Li, Fang; Pi, Li; Zeng, Tieyong

doi:10.1109/lsp.2012.2206582

Cited by 18 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used wavelet shrinkage [48], wavelet shrinkage and adaptive elliptical kernel for image smoothing [49], and generalized shrinkage-threshold for deblurring images [50]. The Lepski's methods like 1D estimator [51], minimax adaptive estimation [52], spatial adaptation inhomogeneous smoothness [53], and pointwise adaptive [54] are adopted in our paper.…”

Section: Soft-thresholdmentioning

confidence: 99%

Deblocking Artifact of Satellite Image Based on Adaptive Soft-Threshold Anisotropic Filter Using Wavelet

Risnandar

Aritsugi

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYNew deblocking artifact, or blocking artifact reduction, algorithms based on nonlinear adaptive soft-threshold anisotropic filter in wavelet are proposed. Our deblocking algorithm uses soft-threshold, adaptive wavelet direction, adaptive anisotropic filter, and estimation. The novelties of this paper are an adaptive soft-threshold for deblocking artifact and an optimal intersection of confidence intervals (OICI) method in deblocking artifact estimation. The soft-threshold values are adaptable to different thresholds of flat area, texture area, and blocking artifact. The OICI is a reconstruction technique of estimated deblocking artifact which improves acceptable quality level of estimated deblocking artifact and reduces execution time of deblocking artifact estimation compared to the other methods. Our adaptive OICI method outperforms other adaptive deblocking artifact methods. Our estimated deblocking artifact algorithms have up to 98% of MSE improvement, up to 89% of RMSE improvement, and up to 99% of MAE improvement. We also got up to 77.98% reduction of computational time of deblocking artifact estimations, compared to other methods. We have estimated shift and add algorithms by using Euler + +(E + +) and Runge-Kutta of order 4++ (RK4 + +) algorithms which iterate one step an ordinary differential equation integration method. Experimental results showed that our E + + and RK4 + + algorithms could reduce computational time in terms of shift and add, and RK4 + + algorithm is superior to E + + algorithm.

show abstract

Section: Soft-thresholdmentioning

confidence: 99%

Deblocking Artifact of Satellite Image Based on Adaptive Soft-Threshold Anisotropic Filter Using Wavelet

Risnandar

Aritsugi

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Recently, some variational fusion methods [36,41,43] have been introduced based on structure tensor and first-order gradient information. Structure tensor is widely used to enhance coherence in image restoration problems [15,27,47]. Since the first-order method is closely related to the proposed method in this paper, we recall the basic idea of first-order fusion approach below (for details, see [36]).…”

Section: Introductionmentioning

confidence: 99%

Variational Image Fusion with First and Second-Order Gradient Information

Zeng²

2016

JCM

View full text Add to dashboard Cite

Image fusion is important in computer vision where the main goal is to integrate several sources images of the same scene into a more informative image. In this paper, we propose a variational image fusion method based on the first and second-order gradient information. Firstly, we select the target first-order and second-order gradient information from the source images by a new and simple salience criterion. Then we build our model by requiring that the first-order and second-order gradient information of the fused image match with the target gradient information, and meanwhile the fused image is close to the source images. Theoretically, we can prove that our variational model has a unique minimizer. In the numerical implementation, we take use of the split Bregman method to get an efficient algorithm. Moreover, four-direction difference scheme is proposed to discrete gradient operator, which can dramatically enhance the fusion quality. A number of experiments and comparisons with some popular existing methods demonstrate that the proposed model is promising in various image fusion applications.

show abstract

“…The method proposed in [5] combines a fuzzy nonlinear anisotropic diffusion with non-local means to adapt the diffusion coefficients according to the characteristics of the reconstructed image. The PDEs can also be used on coherence enhancing task which is based on the design of structure tensor [14,22,23]. For images with thin edges and low-contrast fine features, a modified anisotropic diffusion is proposed in [4], this diffusion coefficient function incorporates both local features of grey-level variance and gradient.…”

Section: Introductionmentioning

confidence: 99%

Kernel-based fourth-order diffusion for image noise removal

Yang

Zhang

2014

International Journal of Computer Mathematics

View full text Add to dashboard Cite

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourthorder diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.

show abstract

Explicit Coherence Enhancing Filter With Spatial Adaptive Elliptical Kernel

Cited by 18 publications

References 21 publications

Deblocking Artifact of Satellite Image Based on Adaptive Soft-Threshold Anisotropic Filter Using Wavelet

Deblocking Artifact of Satellite Image Based on Adaptive Soft-Threshold Anisotropic Filter Using Wavelet

Variational Image Fusion with First and Second-Order Gradient Information

Kernel-based fourth-order diffusion for image noise removal

Contact Info

Product

Resources

About