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Spatial Adaptive Filter Network With Scale-Sharing Convolution for Image Demoireing
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AI
DOI：10.1109/LSP.2024.3451948.png)
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Abstract
En 中文
Removing moire patterns is a challenging task as it is a spatially varying degradation that varies in shape, color and scale. Existing image restoration models often rely on static convolutional neural networks (CNNs)-based architectures, and hence potentially suboptimal for addressing the diverse manifestations of moire patterns across different images and spatial positions. To this end, we propose a spatially adaptive neural network for image demoireing. This network introduces a dual-branch filter prediction module engineered to predict pixel-wise adaptive filters that can process moire patterns of varying orientations and color-shift issues. To further tackle the challenge presented by scale variability, a scale-sharing convolution module is proposed, utilizing pixel-wise adaptive filters with multiple dilations to handle moire patterns of different sizes but similar shapes effectively. Upon extensive evaluations of three benchmark datasets, our model consistently outperforms existing methods, yielding a PSNR improvement of over 0.37dB across all evaluated datasets and providing additional benefits in terms of model size.
Keywords:
Convolution
Feature extraction
Adaptive filters
Image color analysis
Shape
Tensors
Semantics
Moir & eacute
pattern removal
dynamic filtering
spatial-varying processing
Journal
IF:
9.6
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1.1W
Citations:
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