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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

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Related Experiment Video

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Landmark image super-resolution by retrieving web images.

Huanjing Yue, Xiaoyan Sun, Jingyu Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 27, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel super-resolution (SR) method for landmark images using web image retrieval. This approach significantly enhances image detail and quality by leveraging correlated online high-resolution (HR) images.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Super-resolution (SR) techniques aim to enhance image resolution.
    • Exemplar-based SR methods benefit from external high-resolution (HR) image data.

    Purpose of the Study:

    • To develop an improved super-resolution scheme for landmark images.
    • To leverage correlated web images for enhanced exemplar-based SR.

    Main Methods:

    • Extracting local descriptors from up-sampled low-resolution (LR) images.
    • Retrieving correlated HR images from the web based on spatial relationships.
    • Employing global registration for image alignment to mitigate variations in illumination and perspective.
    • Utilizing a structure-aware matching criterion and adaptive block sizes for accurate LR-HR patch mapping.
    • Reconstructing the HR image by blending matched HR patches via energy minimization.

    Main Results:

    • The proposed SR scheme demonstrates significant improvements over existing state-of-the-art methods.
    • Enhanced subjective and objective image quality metrics were achieved.
    • Global registration effectively reduced mismatching due to variations in retrieved web images.

    Conclusions:

    • Retrieving and aligning correlated web images is an effective strategy for exemplar-based super-resolution.
    • The proposed method offers a robust solution for enhancing landmark image resolution.