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Learning Controllable ISP for Image Enhancement.

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    |August 21, 2023
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    Summary
    This summary is machine-generated.

    ContRollable Image Signal Processor (CRISP) enhances images by controlling ISP parameters for diverse styles efficiently. This method achieves high quality and style control with significantly fewer parameters and computations than prior work.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Image Signal Processors (ISPs) are crucial for image quality.
    • Existing methods struggle to efficiently produce diverse image styles.
    • Controlling ISP parameters directly for style is challenging.

    Purpose of the Study:

    • To introduce a plug-and-play Image Signal Processor (ISP) for diverse image style enhancement.
    • To develop a method that explicitly controls ISP parameters for style.
    • To achieve efficient and controllable image style adjustments.

    Main Methods:

    • Proposed ContRollable Image Signal Processor (CRISP) method.
    • Encoding ISP parameters for high-quality (HQ) image styles into low-dimensional latent codes.
    • Empirical evaluation on the MIT-Adobe FiveK dataset.

    Main Results:

    • CRISP covers a wide range of image styles with high efficiency.
    • CRISP closely estimates reference styles from human experts.
    • Achieved better Mean Opinion Scores (MOS) with diverse image styles.
    • CRISP has 2× fewer parameters and 100× reduced FLOPs compared to state-of-the-art methods.
    • Outperforms previous works in PSNR and FLOPs for style adjustments.

    Conclusions:

    • CRISP offers efficient and controllable image style enhancement.
    • The method provides a significant reduction in computational cost and parameter count.
    • CRISP enables high-quality image style production with user-friendly control.