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Performance comparison of image enhancers with and without deep learning.

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    |April 26, 2022
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    Deep learning image enhancement methods produce less noisy results than traditional techniques for poorly illuminated images. This noise difference is crucial for computer vision tasks like image retrieval, impacting algorithms such as SIFT and ORB.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Image enhancement algorithms aim to improve the visibility of image details and content.
    • Traditional methods rely on physical models, while deep learning techniques learn from data.
    • Poorly illuminated images present a significant challenge for visual analysis.

    Purpose of the Study:

    • To empirically compare traditional and deep learning image enhancement methods.
    • To evaluate the impact of these enhancement techniques on image retrieval performance.
    • To promote informed usage of image enhancers in computer vision applications.

    Main Methods:

    • Selection of representative traditional and deep learning image enhancement algorithms.
    • Experiments conducted on public datasets featuring poorly illuminated images.
    • Evaluation of image enhancement techniques using image retrieval algorithms (SIFT, ORB).

    Main Results:

    • All tested enhancers improved image visibility and detail.
    • Deep learning methods generally produced images with lower noise levels compared to traditional methods.
    • Noise characteristics of enhanced images significantly affected image retrieval accuracy for SIFT and ORB.

    Conclusions:

    • Deep learning-based image enhancement offers advantages in noise reduction for poorly illuminated images.
    • The choice of enhancement method should consider the noise sensitivity of downstream computer vision tasks.
    • Understanding enhancer impact is critical for optimizing applications like image retrieval.