Name
Title | ||
|---|---|---|
Using channel representations in regularization terms a case study on image diffusion |
Abstract | ||
|---|---|---|
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.5220/0004667500480055 | 2014 International Conference on Computer Vision Theory and Applications (VISAPP) |
Keywords | Field | DocType |
Image Enhancement,Channel Representation,Channel Smoothing,Diffusion,Energy Minimization | Iterative reconstruction,Noise reduction,Anisotropic diffusion,Computer vision,Weighting,Pattern recognition,Non-local means,Computer science,Filter (signal processing),Artificial intelligence,Energy functional,Gaussian noise | Conference |
Volume | Citations | PageRank |
1 | 1 | 0.35 |
References | Authors | |
18 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Heinemann | 1 | 1 | 0.69 |
| Freddie Åström | 2 | 51 | 9.04 |
| George Baravdish | 3 | 15 | 5.13 |
| Kai Krajsek | 4 | 57 | 7.30 |
| Michael Felsberg | 5 | 2419 | 130.29 |
| Hanno Scharr | 6 | 430 | 37.92 |