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    This summary is machine-generated.

    This study introduces ZITS++, an enhanced image inpainting model that effectively restores vivid textures and structures in corrupted images by leveraging structural priors and Fourier-based texture restoration. The model significantly improves high-resolution image inpainting capabilities.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Image inpainting, the process of reconstructing missing image regions, faces challenges in simultaneously restoring vivid textures and coherent structures.
    • Existing Convolutional Neural Network (CNN) methods often struggle with holistic structural restoration due to limited receptive fields, primarily focusing on regular textures.

    Purpose of the Study:

    • To introduce ZITS++, an improved image inpainting model that addresses limitations of previous methods by integrating structural priors and advanced texture restoration techniques.
    • To enhance the stability and inpainting performance for high-resolution images through novel architectural components and optimization strategies.

    Main Methods:

    • Developed the Transformer Structure Restorer (TSR) module for low-resolution structural prior restoration and the Simple Structure Upsampler (SSU) for resolution enhancement.
    • Employed the Fourier CNN Texture Restoration (FTR) module, enhanced with Fourier and large-kernel attention convolutions, for recovering texture details.
    • Integrated the Structure Feature Encoder (SFE) and Zero-initialized Residual Addition (ZeroRA) for optimizing texture restoration using structural priors, alongside a novel masking positional encoding.

    Main Results:

    • ZITS++ demonstrates improved stability and inpainting ability compared to its predecessor, ZITS.
    • The study provides a comprehensive exploration of various image priors for inpainting and their effective utilization in high-resolution scenarios.
    • Extensive experiments validate the model's effectiveness in restoring both structural integrity and textural details in corrupted images.

    Conclusions:

    • ZITS++ represents a significant advancement in image inpainting, particularly for high-resolution applications, by effectively combining structural and textural restoration.
    • The investigation into image priors offers valuable insights for the broader research community, paving the way for future improvements in image restoration techniques.