Abstract
Saliency maps provide a measurement of people’s attention to images. People pay more attention to salient regions and perceive more information in them. Image denoising enhances image quality by reducing the noise in contaminated images. Here we implement an algorithm framework to use a saliency map as weight to manage tradeoffs in denoising using sparse coding. Computer simulations confirm that the proposed method achieves better performance than a method without the saliency map.
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Zhao, H., Zhang, L. (2011). Sparse Coding Image Denoising Based on Saliency Map Weight. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_36
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DOI: https://doi.org/10.1007/978-3-642-24958-7_36
Publisher Name: Springer, Berlin, Heidelberg
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