Abstract
A method for reconstruction and restoration of super resolution images from sequences of noisy low-resolution images is presented. After estimating the projective transformation parameters between a selected reference image and the observed degraded image sequence frames, the data is rearranged into a sequence with only quantized sub pixel translations. Next, the imaging system's point spread function (PSF) and the auto-correlation function of the image are estimated with a resolution higher than that of the super resolution image. The coefficients of the FIR Wiener filter are computed, low-pass filtered, and decimated so a polyphase filter bank is obtained. Each one of the images in the translated rearranged sequence is filtered by its corresponding polyphase filter. These filtering results are summed and locally normalized according to the apparent data. The super resolution result is refined by estimating the values of pixels that could not be reconstructed by interpolation. The use of the polyphase filters allows exploitation of the input data without any averaging operations needed when implementing conventional FIR Wiener filtering. The presented experimental results show good resolution improvement in presence of noise.
| Original language | English |
|---|---|
| Pages (from-to) | 504-514 |
| Number of pages | 11 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 3808 |
| State | Published - 1 Dec 1999 |
| Event | Proceedings of the 1999 Applications of Digital Image Processing XXII - Denver, CO, USA Duration: 20 Jul 1999 → 23 Jul 1999 |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering