Abstract
We design an image quality measure independent of contrast changes, which are defined as a set of transformations preserving an order between the level lines of an image. This problem can be expressed as an isotonic regression problem. Depending on the definition of a level line, the partial order between adjacent regions can be defined through chains, polytrees or directed acyclic graphs. We provide a few analytic properties of the minimizers and design original optimization procedures together with a full complexity analysis. The methods worst case complexities range from O(n) for chains, to O(nlog n) for polytrees and O(n2ϵ) for directed acyclic graphs, where n is the number of pixels and ϵ is a relative precision. The proposed algorithms have potential applications in change detection, stereo-vision, image registration, color image processing or image fusion. A C++ implementation with Matlab headers is available at https://github.com/pierre-weiss/contrast_invariant_snr.
Original language | English |
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Journal | International Journal of Computer Vision |
Volume | 127 |
Issue number | 8 |
Pages (from-to) | 1144-1161 |
Number of pages | 18 |
ISSN | 0920-5691 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- Convex optimization
- Dynamic programming
- Illumination invariance
- Image quality measure
- Isotonic regression
- Local contrast change
- Signal-to-noise-ratio
- Topographic map