Mixed anisotropic diffusion

Terebes, Romulus; Lavialle, Olivier; Baylou, Pierre; Borda, Monica

doi:10.1109/icpr.2002.1048102

Cited by 11 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the latter is faster since the eigenvalues can be extracted in closed form [5], [26]. In principle this would also allow to combine explicitly isotropic and anisotropic diffusion [42], but we chose to apply the same scheme to edge-like images (e.g. the pepper image) and fingerprint images since the achieved results were satisfying in both cases.…”

Section: E Practical Detailsmentioning

confidence: 99%

Autocorrelation-Driven Diffusion Filtering

Felsberg

2011

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-In this paper we present a novel scheme for anisotropic diffusion driven by the image autocorrelation function. We show the equivalence of this scheme to a special case of iterated adaptive filtering. By determining the diffusion tensor field from an autocorrelation estimate, we obtain an evolution equation that is computed from a scalar product of diffusion tensor and the image Hessian. We propose further a set of filters to approximate the Hessian on a minimized spatial support. On standard benchmarks, the resulting method performs favorable in many cases, in particular at low noise levels. In a GPU implementation video real-time performance is easily achieved.

show abstract

Section: E Practical Detailsmentioning

confidence: 99%

Autocorrelation-Driven Diffusion Filtering

Felsberg

2011

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Based on these classical approaches, Terebes et al (2002) proposed a new model, which takes advantage of both scalar and tensor driven diffusions. The mixeddiffusion combines the CED model with an original approach of the Perona Malik filter.…”

Section: Introductionmentioning

confidence: 99%

Seismic fault preserving diffusion

Lavialle

Pop²,

Germain

et al. 2007

Journal of Applied Geophysics

View full text Add to dashboard Cite

This paper focuses on the denoising and enhancing of 3-D reflection seismic data. We propose a pre-processing step based on a non-linear diffusion filtering leading to a better detection of seismic faults. The non-linear diffusion approaches are based on the definition of a partial differential equation that allows us to simplify the images without blurring relevant details or discontinuities. Computing the structure tensor which provides information on the local orientation of the geological layers, we propose to drive the diffusion along these layers using a new approach called SFPD (Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor are fixed according to a confidence measure that takes into account the regularity of the local seismic structure. Results on both synthesized and real 3-D blocks show the efficiency of the proposed approach.

show abstract

“…A region is defined to be non-oriented when its orientation coherence is less than a pre-determined threshold; this coherence function is a measure of how uniform gradient directions are around a pixel. In [11], the coherence of f is measured directly using a small window W around each pixel by the formula…”

Section: Orientation Coherence and Oriented Region Determinationmentioning

confidence: 99%

Introducing oriented Laplacian diffusion into a variational decomposition model

Shahidi

Moloney

2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

The decomposition model proposed by Osher, Solé and Vese in 2003 (the OSV model) is known for its good denoising performance. This performance has been found to be due to its higher weighting of lower image frequencies in the H −1 -norm modeling the noise component in the model. However, the OSV model tends to also move high-frequency texture into this noise component. Diffusion with an oriented Laplacian for oriented texture is introduced in this paper, in lieu of the usual Laplacian operator used to solve the OSV model, thereby significantly reducing the presence of such texture in the noise component. Results obtained from the proposed oriented Laplacian model for test images with oriented texture are given, and compared to those from the OSV model as well as the Mean Curvature model (MCM). In general, the proposed oriented Laplacian model yields higher signal-to-noise ratios and visually superior denoising results than either the OSV or the MCM models. We also compare the proposed method to a non-local means model and find that although the proposed method generally yields slightly lower signal-to-noise ratios, it generally gives results of better perceptual visual quality.

show abstract

Mixed anisotropic diffusion

Cited by 11 publications

References 11 publications

Autocorrelation-Driven Diffusion Filtering

Autocorrelation-Driven Diffusion Filtering

Seismic fault preserving diffusion

Introducing oriented Laplacian diffusion into a variational decomposition model

Contact Info

Product

Resources

About