Abstract

We call methods for achieving high-resolution enlargements of pixel-based images super-resolution algorithms. Many applications in graphics or image processing could benefit from such resolution independence, including image-based rendering (IBR), texture mapping, enlarging consumer photographs, and converting NTSC video content to high-definition television. We built on another training-based super-resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution. Our algorithm requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data. This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. We don't expect perfect resolution independence-even the polygon representation doesn't have that-but increasing the resolution independence of pixel-based representations is an important task for IBR.

Keywords

Computer scienceArtificial intelligencePixelRendering (computer graphics)Computer graphicsImage resolutionComputer visionResolution (logic)Image textureComputer graphics (images)Image (mathematics)Image processing

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Publication Info

Year
2002
Type
article
Volume
22
Issue
2
Pages
56-65
Citations
2502
Access
Closed

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William T. Freeman, Thouis R. Jones, Egon Pasztor (2002). Example-based super-resolution. IEEE Computer Graphics and Applications , 22 (2) , 56-65. https://doi.org/10.1109/38.988747

Identifiers

DOI
10.1109/38.988747