Stereoscopic Image Super-Resolution with Stereo Consistent Feature
| Autor: | Sungil Choi, Wonil Song, Kwanghoon Sohn, Somi Jeong |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pixel
Computer science business.industry Aggregate (data warehouse) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Stereoscopy 02 engineering and technology General Medicine Superresolution GeneralLiterature_MISCELLANEOUS Image (mathematics) law.invention Constraint (information theory) law Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Parallax ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | We present a first attempt for stereoscopic image super-resolution (SR) for recovering high-resolution details while preserving stereo-consistency between stereoscopic image pair. The most challenging issue in the stereoscopic SR is that the texture details should be consistent for corresponding pixels in stereoscopic SR image pair. However, existing stereo SR methods cannot maintain the stereo-consistency, thus causing 3D fatigue to the viewers. To address this issue, in this paper, we propose a self and parallax attention mechanism (SPAM) to aggregate the information from its own image and the counterpart stereo image simultaneously, thus reconstructing high-quality stereoscopic SR image pairs. Moreover, we design an efficient network architecture and effective loss functions to enforce stereo-consistency constraint. Finally, experimental results demonstrate the superiority of our method over state-of-the-art SR methods in terms of both quantitative metrics and qualitative visual quality while maintaining stereo-consistency between stereoscopic image pair. |
| Databáze: | OpenAIRE |
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