Name
Abstract | ||
|---|---|---|
In-line holography enables particle measurements in large imaging volumes with an extended depth of field compared with conventional imaging systems. Accurate measurements of the structural details of the particles are practically possible only if the measured details are brought in focus. In order to extract in-focus images of objects recorded into a digital hologram from a large set of holograms in a feasible manner, automated focusing and segmentation are needed. The twin image present inheritently in the in-line holography data leads to extra noise that makes this a challenging problem. We propose a new depth estimation method for in-line holography, where the stack of reconstructed intensity images is analyzed. First rough object locations are estimated where the object depths are estimated with a wavelet based focus measure. Clusters of depth estimates are used with plane fitting to approximate the object orientation in the 3D volume to obtain the final all-in-focus images. We show that the proposed method can be used to obtain sharp images of planar objects, such as snowflakes. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC) | noise,image reconstruction,holography |
Field | DocType | Citations |
Iterative reconstruction,Holography,Computer vision,Cluster (physics),Segmentation,Snowflake,Optics,Planar,Artificial intelligence,Mathematics,Wavelet,Depth of field | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sami Varjo | 1 | 3 | 2.14 |
| ville a kaikkonen | 2 | 0 | 1.01 |
| Jari Hannuksela | 3 | 121 | 13.36 |
| anssi j makynen | 4 | 0 | 2.03 |