Reliability-based mesh-to-grid image reconstruction
| Autor: | Andre Kaup, Jan Koloda, Jurgen Seiler |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Noise reduction Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology Iterative reconstruction I.4.3 I.4.5 01 natural sciences 010309 optics 0103 physical sciences FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Computer vision Image warping Image resolution Reliability (statistics) business.industry Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing Grid Visualization Computer Science::Computer Vision and Pattern Recognition 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | MMSP |
| DOI: | 10.1109/mmsp.2016.7813344 |
| Popis: | This paper presents a novel method for the reconstruction of images from samples located at non-integer positions, called mesh. This is a common scenario for many image processing applications, such as super-resolution, warping or virtual view generation in multi-camera systems. The proposed method relies on a set of initial estimates that are later refined by a new reliability-based content-adaptive framework that employs denoising in order to reduce the reconstruction error. The reliability of the initial estimate is computed so stronger denoising is applied to less reliable estimates. The proposed technique can improve the reconstruction quality by more than 2 dB (in terms of PSNR) with respect to the initial estimate and it outperforms the state-of-the-art denoising-based refinement by up to 0.7 dB. |
| Databáze: | OpenAIRE |
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