Abstract
In this paper, we present a novel image upsampling method within a two-stage framework to reconstruct different image content (large-scale edges and small-scale structures). First, we utilize a total variation (TV) filter for image decomposition which decomposes an image content into structure component and texture component. In the first stage, the structure component is enhanced by a shock filter and an improved non-local means filter, then combines with the texture component to generate initial high-resolution (HR) image. In the second stage, the gradient of initial HR image is regarded as an edge preserving constraint to reconstruct the texture component. Experimental results demonstrate that the new approach can reconstruct faithfully the HR images with sharp edges and texture structures, and annoying artifacts (blurring, jaggies, ringing, etc.) are greatly suppressed. It outperforms the state-of-the-art approaches, based on subjective and objective evaluations.
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Acknowledgements
This work is partially supported by the National High Technology Research and Development Program of China (863 Program) under contract No. 2015AA015903, the National Science Foundation of China (61421062, 61502013), the Major National Scientific Instrument and Equipment Development Project of China under contract No. 2013YQ030967.
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Yang, F., Jia, H., Xie, D., Chen, R., Gao, W. (2018). Content Adaptive Constraint Based Image Upsampling. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_81
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