Small-Kernel Superresolution Methods for Microscanning Imaging Systems
Abstract
Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented. Microscanning creates multiple low-resolution images with slightly different sample scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end, continuous discrete continuous microscanning imaging system model. Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2006
- Accession Number
- ADA478593
Entities
People
- James D. Howe
- Jiazheng Shi
- Stephen E. Reichenbach
Organizations
- University of Nebraska-Lincoln Department of Computer Science and Engineering