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    <responseDate>2026-10-15T06:41:47Z</responseDate>
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                <identifier>oai:data.mendeley.com/pjxpzkhbf7.3</identifier>
                <datestamp>2026-06-23T20:12:36Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>M S, Karthik</dc:creator>
    <dc:title>Blurred Chest X-ray Images: A Simulated Dataset for Image Deblurring and Restoration Benchmarking</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains simulated blurred versions of chest X-ray images, organized into train and test splits, for use in image deblurring and restoration research. Synthetic blur degradation was applied to original chest X-ray images to create paired (sharp/blurred) data suitable for training and evaluating deep learning-based restoration and deblurring models.
The original (non-blurred) chest X-ray images were obtained from the &quot;COVID19, Pneumonia and Normal Chest X-ray PA Dataset&quot; by Amanullah Asraf and Zabirul Islam, published on Mendeley Data (DOI: 10.17632/jctsfj2sfn.1), licensed under CC BY 4.0. We gratefully acknowledge the original contributors for making this resource publicly available.</dc:description>
    <dc:subject>Image Processing</dc:subject>
    <dc:subject>Deep Learning</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/pjxpzkhbf7.3</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/pjxpzkhbf7.3</dc:identifier>
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    <dc:date>2026-06-23T20:12:36Z</dc:date>
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