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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

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Anisotropic Guided Filtering.

Carlo Noel Ochotorena, Yukihiko Yamashita

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 24, 2019

Summary

The novel Anisotropic Guided Filter (AnisGF) overcomes limitations of existing guided filters by using weighted averaging to reduce detail halos and improve handling of structural inconsistencies in image processing.

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Guided filters are popular for image processing due to low complexity and edge preservation.
Existing guided filters struggle with aggressive filtering (detail halos) and structural inconsistencies.
Limitations arise from local-isotropy and unweighted averaging in variants like AGF, WGIF, and GGIF.

Purpose of the Study:

Introduce a novel Anisotropic Guided Filter (AnisGF).
Address detail halos and structural inconsistency issues in guided filtering.
Maintain computational efficiency of original guided filters.

Main Methods:

Developed AnisGF utilizing weighted averaging for enhanced diffusion and edge preservation.
Optimized weights based on local neighborhood variances for strong anisotropic filtering.

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Maintained low computational cost comparable to the original guided filter.

Main Results:

AnisGF effectively mitigates detail halos.
The filter handles structural inconsistencies between input and guide images.
Demonstrated improvements in scale-aware filtering, detail enhancement, texture removal, and chroma upsampling.

Conclusions:

AnisGF offers a significant advancement over existing guided filters.
The weighted averaging approach effectively balances diffusion and edge preservation.
AnisGF shows broad applicability and improved performance in various image processing tasks.