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        <title>Frontiers in Imaging | Image Acquisition and Processing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/imaging/sections/image-acquisition-and-processing</link>
        <description>RSS Feed for Image Acquisition and Processing section in the Frontiers in Imaging journal | New and Recent Articles</description>
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        <pubDate>2026-04-10T08:29:03.672+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1336829</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1336829</link>
        <title><![CDATA[Computational optical imaging: challenges, opportunities, new trends, and emerging applications]]></title>
        <pubdate>2024-02-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Meng Xiang</author><author>Fei Liu</author><author>Jinpeng Liu</author><author>Xue Dong</author><author>Qianqian Liu</author><author>Xiaopeng Shao</author>
        <description><![CDATA[Computational imaging technology (CIT), with its many variations, addresses the limitations of industrial design. CIT can effectively overcome the bottlenecks in physical information acquisition, model development, and resolution by being tightly coupled with mathematical calculations and signal processing in information acquisition, transmission, and interpretation. Qualitative improvements are achieved in the dimensions, scale, and resolution of the information. Therefore, in this review, the concepts and meaning of CIT are summarized before establishing a real CIT system. The basic common problems and relevant challenging technologies are analyzed, particularly the non-linear imaging model. The five typical imaging requirements–distance, resolution, applicability, field of view, and system size–are detailed. The corresponding key issues of super-large-aperture imaging systems, imaging beyond the diffraction limit, bionic optics, interpretation of light field information, computational optical system design, and computational detectors are also discussed. This review provides a global perspective for researchers to promote technological developments and applications.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2023.1133311</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2023.1133311</link>
        <title><![CDATA[Second harmonic generation imaging of head and neck squamous cell carcinoma]]></title>
        <pubdate>2023-11-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Magdalena Stocker</author><author>Philipp Baumeister</author><author>Martin Canis</author><author>Martin Vogel</author><author>Olivier Gires</author>
        <description><![CDATA[Head and neck squamous cell carcinomas (HNSCC) are a heterogenous group of tumors with a poor prognosis and treatments impairing the patients' quality of life. We assessed post-surgical human head and neck squamous cell carcinomas (HNSCC) and healthy tissue (n = 10 each) via HHG (higher harmonic generation) imaging to differentiate healthy from tumor tissue. Qualitative imaging analysis compared collagen fibrils detected via immunohistology and SHG (second harmonic generation). Quantitative analysis measured the forward to backward intensity ratio (fSHG/bSHG) of SHG images. Assessments of the tissue samples demonstrated a structural difference of collagen matrix organization from healthy to malignant tissue. Healthy tissue was characterized by a high f/b ratio, describing highly organized tissue, whereas a low f/b ratio was observed in malignant tissue, indicative of reduced organization. Properly distinguishing tumor from healthy tissue is crucial to a successful treatment and best possible outcome for the individual patient. SHG provides broad possibilities to analyze extracellular changes in diseased tissue, such as solid tumors and to distinguish tumor from healthy tissue.]]></description>
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