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Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

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Image completion by diffusion maps and spectral relaxation.

Shai Gepshtein¹, Yosi Keller

¹Faculty of Engineering, Bar Ilan University, Ramat Gan 52900, Israel. shaigep@gmail.com

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

|January 17, 2013

Summary

This study introduces a novel image inpainting framework using spectral dimensionality reduction. The method simplifies inpainting for textured images by leveraging induced smoothness in an embedding domain, outperforming existing techniques.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Image inpainting aims to reconstruct missing image regions.
Existing methods often struggle with complex textures and require sophisticated algorithms.

Purpose of the Study:

To develop a novel image inpainting framework using spectral dimensionality reduction.
To demonstrate improved inpainting performance, especially for textured images.

Main Methods:

Formulating the inpainting problem in a diffusion embedding domain.
Utilizing induced smoothness for simpler inpainting techniques (exemplar-based, variational).
Developing a novel computational approach for inverse mapping via discrete optimization and spectral relaxation.

Main Results:

The embedding domain exhibits enhanced smoothness, particularly for textured images.
The proposed method effectively inpaints real images.
Favorable comparison with contemporary state-of-the-art inpainting schemes.

Conclusions:

The diffusion framework combined with spectral dimensionality reduction offers an effective approach to image inpainting.
Induced smoothness in the embedding domain simplifies the inpainting process for textured regions.
The novel inverse mapping technique successfully reconstructs images from the inpainted embedding space.