Name
Abstract | ||
|---|---|---|
Increasing the spatial resolution is an ongoing research topic in image processing. A recently presented approach applies a non-regular sampling mask on a low resolution sensor and subsequently reconstructs the masked area via an extrapolation algorithm to obtain a high resolution image. This paper introduces an acceleration of this approach for use with full color sensors. Instead of employing the effective, yet computationally expensive extrapolation algorithm on each of the three RGB channels, a color space conversion is performed and only the luminance channel is then reconstructed using this algorithm. As natural images contain much less information in the chrominance channels, a fast linear interpolation technique can here be used to accelerate the whole reconstruction procedure. Simulation results show that an average speed up factor of 2.9 is thus achieved, while the loss in visual quality stays imperceptible. Comparisons of PSNR results confirm this. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VCIP.2014.7051543 | Valletta |
Keywords | Field | DocType |
brightness,extrapolation,image colour analysis,image reconstruction,image resolution,image sensors,interpolation,PSNR,RGB channels,chrominance channels,color space conversion,extrapolation algorithm,high resolution image,hybrid image reconstruction,image processing,linear interpolation technique,low resolution sensor,luminance channel,natural images,nonregular sampling color sensors,nonregular sampling mask,reconstruction procedure,spatial resolution,visual quality,Color Image Sensor,Color Space Conversion,Image Reconstruction,Non-Regular Sampling,Signal Extrapolation | Computer vision,Color space,Color histogram,Computer science,Color balance,Demosaicing,Color depth,RGB color model,Artificial intelligence,Image resolution,Color image | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Batz, M. | 1 | 1 | 0.36 |
| Andrea Eichenseer | 2 | 5 | 2.33 |
| Jonscher, M. | 3 | 1 | 0.36 |
| Seiler, J. | 4 | 7 | 1.84 |