Near-lossless L-infinity constrained Multi-rate Image Decompression via Deep Neural Network
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Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decompressed patches to the original counterparts under the ubiquitous L2 metric. However, this approach is incapable of restoring distinctive image details which may be statistical outliers but have high semantic importance (e.g., tiny lesions in medical images). To overcome this weakness, we propose to incorporate an L-infinity fidelity criterion in the design of neural network so that no small, distinctive structures of the original image can be dropped or distorted. Moreover, our anti-artifacts neural network is designed to work on a range of compression bit rates, rather than a fixed one as in the past. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in both L2 and L-infinity error metrics, and also perceptually. It can restore subtle image details that are otherwise destroyed or missed by other algorithms. Our research suggests a new machine learning paradigm of ultra high fidelity image compression that is ideally suited for applications in medicine, space, and sciences.
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