Towards Flexible Blind JPEG Artifacts Removal
This addresses the need for flexible blind JPEG artifact removal in practical image processing applications, representing an incremental improvement over existing blind methods.
The paper tackles the problem of training a single deep blind model to remove JPEG artifacts across different quality factors, proposing FBCNN which predicts adjustable quality factors to control the trade-off between artifact removal and detail preservation. The method achieves favorable performance against state-of-the-art methods on single JPEG images, double JPEG images, and real-world JPEG images.
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.