Publication2018
Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 April 2018
- Level
- National
- Journal
- Optik
- Volume
- 159
- Pages
- 333-343
- Published
- 1 April 2018
- DOI
- 10.1016/j.ijleo.2018.01.096
- Scopus ID
- 85041376880
- ISSN
- 00304026
- Authors
- 3
- Open access
- No
- Citations
- 26
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Image denoising research using weighted bilateral filter and curvelet transform to preserve geometric components, advancing image processing and computer vision research methodology
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Image denoising research using weighted bilateral filter and curvelet transform to preserve geometric components, advancing image processing and computer vision research methodology
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform”. Kortex.