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Joint Reflection Removal and Depth Estimation From a Single Image.

Yakun Chang, Cheolkon Jung, Jun Sun

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    This study introduces a novel neural network for simultaneous reflection removal and depth estimation from single images. The method effectively recovers images degraded by glass reflections and accurately estimates scene depth.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Glass reflections degrade image quality and hinder accurate depth estimation.
    • Existing methods often address reflection removal and depth estimation separately, limiting performance.
    • Single-image depth estimation is challenging, especially in the presence of occlusions like reflections.

    Purpose of the Study:

    • To propose a joint method for simultaneous reflection removal and depth estimation from a single image.
    • To develop a collaborative neural network architecture that integrates these two tasks.
    • To improve the accuracy of both reflection removal and depth estimation in challenging scenarios.

    Main Methods:

    • A collaborative neural network with four blocks: encoder, reflection removal subnetwork (RRN), depth estimation subnetwork (DEN), and depth refinement.
    • Joint training of RRN and DEN by concatenating intermediate features, enabling mutual benefit.
    • Guided image filtering using the recovered transmission layer for depth refinement.

    Main Results:

    • The proposed method successfully removes reflections and estimates depth from single images, even with dominant reflections.
    • The collaborative approach enhances performance compared to separate methods.
    • Accurate object edges from the transmission layer improve depth estimation accuracy.

    Conclusions:

    • Joint reflection removal and depth estimation is feasible and effective using a collaborative neural network.
    • The proposed method offers a unified approach to tackling image degradation and depth perception challenges.
    • This work provides a new perspective on image reflection processing and depth estimation.