  Training a single deep blind model to handle different quality factors for
JPEG image artifacts removal has been attracting considerable attention due to
its convenience for practical usage. 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. To
remedy this problem, in this paper, we 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.
Specifically, FBCNN decouples the quality factor from the JPEG image via a
decoupler module and then embeds the predicted quality factor into the
subsequent reconstructor module through a quality factor attention block for
flexible control. Besides, we find existing methods are prone to fail on
non-aligned double JPEG images even with only a one-pixel shift, and we 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.
