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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1203-1214 |
| Number of pages | 12 |
| Journal | Applied optics |
| Volume | 45 |
| Issue number | 6 |
| DOIs | |
| State | Published - Feb 20 2006 |
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ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
Cite this
Small-kernel superresolution methods for microscanning imaging systems. / Shi, Jiazheng; Reichenbach, Stephen E; Howe, James D.
In: Applied optics, Vol. 45, No. 6, 20.02.2006, p. 1203-1214.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Small-kernel superresolution methods for microscanning imaging systems
AU - Shi, Jiazheng
AU - Reichenbach, Stephen E
AU - Howe, James D.
PY - 2006/2/20
Y1 - 2006/2/20
N2 - 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.
AB - 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.
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U2 - 10.1364/AO.45.001203
DO - 10.1364/AO.45.001203
M3 - Article
VL - 45
SP - 1203
EP - 1214
JO - Applied Optics
JF - Applied Optics
SN - 1559-128X
IS - 6
ER -