Data · dataset · 2025
Data set and codes for the test of the high frequency emphasized neural network reconstruction method
Listed in ScienceDB
It contains simulation and experimental date for the validation of the high frequency emphasized neural network reconstruction method.
Description
The simulation data is a series of images containing randomly generated particles with different size and different location. The experimental data was obtained at BL13W1 beamline of Shanghai Synchrotron Radiation Facility (SSRF) which contains a series of particle samples’ tomograms.
The particles are cooper or ZnO powder. The X-ray energy used was 38KeV, the spatial resolution of the original experimental images was 0.33μm/pixel. All slices are reshaped with the size of 256×256, and the bit depth is 8-bit.
Read the rest (2 more)
The codes for evaluate the reconstruction result in the manuscript are also provided. The file calculate_SSIM.m is used for calculating structural similarity index (SSIM). The file calculate_SSIM.D is used for calculating normalized mean square criterion D. And the file calculate_SSIM.D is used for calculating normalized average absolute distance criterion R. All codes are supposed to be runned in Matlab.
By using the simulation and experimental data, and running the codes, the readers can conduct the validation of reconstruction results.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00186.00761 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Image 65% · Machine learning 70% · Simulation 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00186.00761 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4611 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (70%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |