Flexible artifact removal in JPEG images
Flexible artifact removal in JPEG images
Towards Flexible Blind JPEG Artifacts Removal
arXiv paper abstract https://arxiv.org/abs/2109.14573
arXiv PDF paper https://arxiv.org/pdf/2109.14573.pdf
Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention
... However, existing deep blind methods usually directly reconstruct the image without predicting the quality factor, thus lacking the flexibility to control the output as the non-blind methods.
... propose a flexible blind convolutional neural network, namely FBCNN, that can predict the adjustable quality factor to control the trade-off between artifacts removal and details preservation.
... existing methods are prone to fail on non-aligned double JPEG images ... thus propose a double JPEG degradation model to augment the training data.
Extensive experiments on single JPEG images, more general double JPEG images, and real-world JPEG images demonstrate that our proposed FBCNN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality.
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