Abstract
The common image denoising methods only consider how to restore well image information from noise images, but neglect the effects of residual information between restored images and given images. To enhance denoised image’s quality, a new image denoising method considering residual information in different frequency bands is discussed in this paper. In this method, an original image is divided into high and low frequency sub-band images by the contourlet transform algorithm. And each sub-band image is first denoised by the K-singular value decomposition (K-SVD) denoising model, thus each residual sub-band image is correspondingly obtained. Further, each residual image is again denoised by K-SVD denoising model. Finally, for each sub-band image denoised and its residual image, the inverse transform of contourlet transform algorithm is used to restore the original image. Compared our method proposed here with common denoising methods of wavelet, contourlet, K-SVD, experimental results show that our method fusing residual information in different frequency bands behaves better denoising effect.
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Acknowledgement
This work was supported by the grants from National Nature Science Foundation of China (Grant No. 61373098 and 61370109), the grant from Natural Science Foundation of Anhui Province (No. 1308085MF85).
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Su, Pg., Liu, T., Sun, Zl. (2016). K-SVD Based Image Denoising Method Using Image Residual Information in Different Frequency Bands. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_43
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DOI: https://doi.org/10.1007/978-3-319-42294-7_43
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