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CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks.

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    This study introduces CIE XYZ Net, a deep learning model that converts nonlinear images to a device-independent CIE XYZ color space. This enables improved performance in computer vision tasks like image deblurring and dehazing.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Cameras provide raw-RGB or processed sRGB images, but many computer vision tasks require linear raw data.
    • Existing methods to convert nonlinear images back to raw-RGB are sensor-specific, limiting their general applicability.
    • A canonical image state within the camera pipeline, CIE XYZ, is not directly accessible but can be leveraged.

    Purpose of the Study:

    • To develop a sensor-agnostic deep learning framework for converting nonlinear images to a canonical CIE XYZ color space.
    • To enable the application of advanced computer vision algorithms to readily available nonlinear images.
    • To improve the performance of low-level computer vision tasks by utilizing the CIE XYZ image state.

    Main Methods:

    • A deep learning framework, CIE XYZ Net, was developed to unprocess nonlinear images.
    • The network targets the intermediate CIE XYZ color space, a device-independent representation.
    • The framework allows for processing in CIE XYZ and re-rendering to nonlinear formats.

    Main Results:

    • CIE XYZ Net successfully converts nonlinear images to the CIE XYZ color space.
    • The framework demonstrates significant performance gains in various low-level computer vision tasks.
    • The approach overcomes the sensor-specificity limitations of previous unprocessing methods.

    Conclusions:

    • Leveraging the CIE XYZ color space provides a versatile intermediate representation for image processing.
    • CIE XYZ Net offers a powerful and generalizable solution for enhancing computer vision tasks.
    • The proposed framework facilitates improved image deblurring, dehazing, and other low-level vision applications.