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Related Experiment Video

Updated: Apr 21, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Single-image super-resolution via linear mapping of interpolated self-examples.

Marco Bevilacqua, Aline Roumy, Christine Guillemot

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 28, 2014
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    Summary

    This study introduces a new single-image superresolution method that creates its own training data from the input low-resolution (LR) image. This approach achieves visually pleasing high-resolution (HR) results with sharp details.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Single-image superresolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs.
    • Traditional methods often rely on external dictionaries of image examples, limiting their adaptability.

    Purpose of the Study:

    • To present a novel example-based SISR method that does not require external image dictionaries.
    • To develop an algorithm that generates its own self-examples for training.

    Main Methods:

    • A double pyramid of recursively scaled and interpolated images is generated from the LR input.
    • Self-examples are extracted from this pyramid to learn specialized linear mapping functions.
    • A multipass upscaling procedure with iterative back projection is employed for consistency.

    Main Results:

    • The proposed algorithm produces visually pleasant HR images with sharp edges and well-reconstructed details.
    • Objective metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), demonstrate superior performance compared to state-of-the-art methods.
    • The method excels in both visual quality and quantitative evaluation.

    Conclusions:

    • The developed self-example-based SISR method offers a powerful alternative to dictionary-dependent approaches.
    • This technique effectively reconstructs high-frequency details and improves image quality without external data.
    • The algorithm achieves state-of-the-art performance in single-image superresolution.