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Context Adaptive Network for Image Inpainting.

Ye Deng, Siqi Hui, Sanping Zhou

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 28, 2023

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This study introduces Context Adaptive Network (CANet) for image inpainting, effectively handling irregular damaged areas. CANet generates clearer, more coherent results than existing methods by adaptively balancing features.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Irregularly damaged image regions pose challenges for standard image inpainting models.
Vanilla convolutions struggle with random damage, leading to color discrepancies and blurriness.
Existing methods lack flexibility in handling diverse damage patterns.

Purpose of the Study:

To propose a novel Context Adaptive Network (CANet) for robust image inpainting.
To address the limitations of traditional convolutional approaches in handling irregular image damages.
To improve the quality and coherence of inpainting results.

Main Methods:

Developed a Context Adaptive Network (CANet) featuring two novel modules: Context Adaptive Block (CAB) and Cross-Scale Contextual Attention (CSCA).

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CAB dynamically balances features using an adaptive term based on damage degree (confidence level/soft mask).

CSCA incorporates cross-scale information transfer and attention mechanisms to generate plausible features for damaged areas.

Main Results:

CANet outperforms state-of-the-art methods in image inpainting tasks.
Achieved clearer, more coherent, and visually plausible inpainting results.
Demonstrated effective handling of diverse and irregular image damages.

Conclusions:

CANet offers a flexible and effective solution for image inpainting, particularly for irregular damages.
The proposed adaptive modules enhance feature representation and long-range dependency modeling.
The method significantly improves the quality of reconstructed images.