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A Context-Based Image Contrast Enhancement Using Energy Equalization With Clipping Limit.

Kankanala Srinivas, Ashish Kumar Bhandari, Puli Kishore Kumar

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    This study introduces energy curve equalization (ECE), a novel image contrast enhancement method using a modified neural network. ECE improves upon traditional histogram equalization by incorporating spatial information for smoother, enhanced images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Traditional histogram equalization (HE) can lead to over-enhancement and noise amplification.
    • Existing contrast enhancement methods often fail to fully utilize spatial information within images.

    Purpose of the Study:

    • To propose a new context-based image contrast enhancement technique called energy curve equalization (ECE).
    • To improve image quality by addressing limitations of conventional histogram equalization.

    Main Methods:

    • Developed a novel contrast enhancement process using energy curve equalization (ECE) with a clipping limit.
    • Utilized a modified Hopfield neural network architecture to compute the energy curve, incorporating spatial adjacency information.
    • Subdivided the clipped energy curve into three regions for individual equalization, creating transfer functions for the final enhanced image.

    Main Results:

    • The proposed ECE method produces a smoother energy curve compared to traditional histograms.
    • A clipping limit, set as the average of mean and median, effectively prevents over-enhancement.
    • Qualitative and quantitative assessments demonstrate the efficiency of ECE compared to conventional histogram equalization techniques.

    Conclusions:

    • ECE offers a superior approach to image contrast enhancement by leveraging spatial information and a smoother energy curve.
    • The clipping limit and regional equalization contribute to more effective and controlled image enhancement.
    • The proposed method shows significant potential for improving image quality in various applications.