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Related Experiment Video

Updated: Apr 30, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

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A regularized model-based optimization framework for pan-sharpening.

Hussein A Aly, Gaurav Sharma

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 25, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an improved pan-sharpening method for satellite imagery, enhancing spatial resolution by fusing multispectral and panchromatic images. The new approach yields superior results compared to existing techniques.

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    Last Updated: Apr 30, 2026

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    7.8K

    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Pan-sharpening enhances multispectral satellite image resolution using a higher-resolution panchromatic image.
    • Existing model-based optimization methods have limitations in improving image quality.

    Purpose of the Study:

    • To develop an improved pan-sharpening formulation for enhanced spatial resolution of multispectral satellite imagery.
    • To introduce a novel objective function and iterative algorithm for joint minimization in pan-sharpening.

    Main Methods:

    • Formulating pan-sharpening as a joint estimation problem to minimize a novel objective function.
    • Incorporating a new regularization term and a complementary highpass filter into the observation models.
    • Developing an iterative algorithm to solve the proposed joint minimization for high-resolution (HR) multispectral image generation.

    Main Results:

    • The proposed joint formulation significantly improves upon previous model-based optimization approaches.
    • Quantitative measures (SNR, SAM, etc.) and visual evaluations confirm superior performance over existing methods.
    • A software implementation of the proposed algorithm is provided.

    Conclusions:

    • The novel pan-sharpening formulation offers superior performance in enhancing multispectral satellite imagery.
    • The introduced regularization term and highpass filter are key to the improved results.
    • The developed iterative algorithm effectively produces high-quality pan-sharpened images.