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Heshalini Rajagopal

As seen in: PLOS ONE
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Articles

Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp

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By Universiti Malaya, Lembah Pantai, Kuala Lumpur, Heshalini Rajagopal
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Asri MAA, Rajagopal H, Mokhtar N, Wan Mohd Mahiyiddin WA, Lim YAL, Iwahashi M, et al. (2026) Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp. PLoS One 21(1): e0341160.
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No-reference quality assessment for image-based assessment of economically important tropical woods

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By of Malaya, Kuala Lumpur, Heshalini Rajagopal, Norrima Mokhtar
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Heshalini Rajagopal , Norrima Mokhtar , Tengku Faiz Tengku Mohmed Noor Izam , Wan Khairunizam Wan Ahmad Heshalini Rajagopal, Norrima Mokhtar, Tengku Faiz Tengku Mohmed Noor Izam, Wan Khairunizam Wan Ahmad Published: May 19, 2020 https://doi.org/10.1371/journal.pone.0233320 Figures Abstract Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples.
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