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Updated: Apr 19, 2026
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X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
Published on: August 20, 2019
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Symmetric smoothing filters from global consistency constraints.
Summary
This study introduces a novel consistency filter for image denoising, improving upon doubly stochastic methods by preserving pixel similarity. The new filter offers superior performance and unifies smoothing filters with spectral graph theory.
Area of Science:
- Computer Vision
- Image Processing
- Signal Processing
Background:
- Patch-based image denoising methods often use weighted averaging of similar pixels.
- Doubly stochastic approximations offer improved symmetric and stable smoothing operators for denoising.
Purpose of the Study:
- Introduce a consistency principle to enhance image denoising filters.
- Develop a novel consistency filter that preserves relative pixel similarities.
- Unify symmetric smoothing filters and spectral graph theory in image denoising.
Main Methods:
- Proposed a consistency filter based on preserving relative pixel similarities.
- The filter is a generalized doubly stochastic matrix.
- Interpreted the consistency filter as a form of Laplacian regularization.
Main Results:
- The consistency filter is symmetric and stable.
- It significantly outperforms the doubly stochastic version in image denoising.
- Achieved high-quality image denoising results.
Conclusions:
- The consistency filter offers a unified approach to image denoising.
- It demonstrates superior performance compared to existing methods.
- Provides a theoretically sound and practically effective denoising solution.
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