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The Retina01:32

The Retina

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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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Retina inspired tone mapping method for high dynamic range images.

Xian-Shi Zhang, Kai-Fu Yang, Jun Zhou

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    |April 1, 2020
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    Summary
    This summary is machine-generated.

    This study introduces a novel tone mapping method inspired by retinal processing to display high dynamic range (HDR) images on standard screens. The technique effectively compresses dynamic range and enhances local contrast, avoiding artifacts.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Neuroscience

    Background:

    • Standard displays have limited dynamic range, hindering the visualization of High Dynamic Range (HDR) images.
    • Retinal processing mechanisms, particularly horizontal cell (HC) receptive field (RF) adaptation, offer insights into dynamic range compression.

    Purpose of the Study:

    • To develop an effective tone mapping method for displaying HDR images on regular screens.
    • To leverage retinal processing principles for improved image dynamic range compression and artifact reduction.

    Main Methods:

    • A novel tone mapping algorithm inspired by retinal horizontal cells (HCs) and bipolar cells (BCs).
    • Adaptive receptive field (RF) size adjustment based on local image stimuli for dynamic range compression.
    • Incorporation of center-surround antagonistic RF structure to enhance local contrast and details.

    Main Results:

    • The proposed method successfully compresses the dynamic range of HDR images locally.
    • Avoidance of halo artifacts around high luminance contrast regions.
    • Significant enhancement of local contrast and image details.

    Conclusions:

    • The developed tone mapping method effectively addresses the limitations of standard screen dynamic range for HDR images.
    • The biologically inspired approach provides competitive results compared to state-of-the-art methods.
    • The method demonstrates robustness across diverse image datasets.