Robust super-resolution by fusion of interpolated frames for color and grayscale images
| Autor: | Russell C. Hardie, Barry K. Karch |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Computer science
Materials Science (miscellaneous) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biophysics General Physics and Astronomy Image processing super-resolution Grayscale image restoration Aliasing Computer vision Physical and Theoretical Chemistry Image restoration Mathematical Physics Color Filter Array Demosaicing Demosaicing business.industry Physics Image Enhancement Subpixel rendering lcsh:QC1-999 image processing Undersampling Color filter array Artificial intelligence business lcsh:Physics |
| Zdroj: | Frontiers in Physics, Vol 3 (2015) |
| ISSN: | 2296-424X |
| DOI: | 10.3389/fphy.2015.00028 |
| Popis: | Multi-frame super-resolution (SR) processing seeks to overcome undersampling issues that can lead to undesirable aliasing artifacts in imaging systems. A key factor in effective multi-frame SR is accurate subpixel inter-frame registration. Accurate registration is more difficult when frame-to-frame motion does not contain simple global translation and includes locally moving scene objects. SR processing is further complicated when the camera captures full color by using a Bayer color filter array (CFA). Various aspects of these SR challenges have been previously investigated. Fast SR algorithms tend to have difficulty accommodating complex motion and CFA sensors. Furthermore, methods that can tolerate these complexities tend to be iterative in nature and may not be amenable to real-time processing. In this paper, we present a new fast approach for performing SR in the presence of these challenging imaging conditions. We refer to the new approach as Fusion of Interpolated Frames (FIF) SR. The FIF SR method decouples the demosaicing, interpolation, and restoration steps to simplify the algorithm. Frames are first individually demosaiced and interpolated to the desired resolution. Next, FIF uses a novel weighted sum of the interpolated frames to fuse them into an improved resolution estimate. Finally, restoration is applied to improve any degrading camera effects. The proposed FIF approach has a lower computational complexity than many iterative methods, making it a candidate for real-time implementation. We provide a detailed description of the FIF SR method and show experimental results using synthetic and real datasets in both constrained and complex imaging scenarios. Experiments include airborne grayscale imagery and Bayer CFA image sets with affine background motion plus local motion. |
| Databáze: | OpenAIRE |
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