Abstract
Although sparse coding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparse coding noise is not tight enough. To suppress the sparse coding noise, we exploit a couple of images to estimate unknown sparse code. There are two main contributions in this paper: The first is to use a reference denoised image and an intermediate denoised image to estimate the sparse coding coefficients of the original image. The second is that we set a threshold to rule out blocks of low similarity to improve the accuracy of estimation. Our experimental results have shown improvements over several state-of-the-art denoising methods on a collection of 12 generic natural images.
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Acknowledgment
This work was supported by Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001 and No. 2014B010117007), Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality (ZDSYS201703031405467), Shenzhen Peacock Plan (20130408-1830 03656), and the grant of National Science Foundation of China (No. U1611461).
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Lin, K., Li, G., Zhang, Y., Zhong, J. (2018). A New Accurate Image Denoising Method Based on Sparse Coding Coefficients. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_1
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DOI: https://doi.org/10.1007/978-3-319-73600-6_1
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