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Dynamic Selection Network for Image Inpainting.

Ning Wang, Yipeng Zhang, Lefei Zhang

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

|January 8, 2021

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This study introduces a novel dynamic selection network (DSNet) for image inpainting, improving realistic content generation. The DSNet effectively utilizes valid image regions, outperforming existing methods.

Area of Science:

Computer Vision
Deep Learning
Image Processing

Background:

Image inpainting is crucial for restoring corrupted images.
Deep learning models, particularly convolutional neural networks, are widely used.
Existing methods struggle with random corruptions due to static convolutions and monotonous normalization.

Purpose of the Study:

To propose a novel dynamic selection network (DSNet) for improved image inpainting.
To address limitations of static convolutions and monotonous normalization in deep learning models.
To enhance the utilization of valid image information during the inpainting process.

Main Methods:

Introduced a novel dynamic selection network (DSNet).
Developed Validness Migratable Convolution (VMC) for flexible feature extraction.

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Implemented Regional Composite Normalization (RCN) for selective feature normalization.

Main Results:

DSNet effectively distinguishes corrupted from valid image regions.
VMC dynamically selects spatial sampling locations, enhancing feature extraction.
RCN selectively normalizes feature regions, combining multiple normalization techniques.
The proposed method outperforms state-of-the-art approaches on public datasets.

Conclusions:

DSNet offers a significant advancement in image inpainting.
The dynamic selection mechanism improves the generation of realistic and detailed images.
The VMC and RCN modules contribute to adaptive feature utilization and normalization.