Abstract
In this paper, we present new wavelet shrinkage methods for image denoising. The methods take advantage of the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. We also investigate a multiplying factor for the universal threshold in order to obtain better denoising results. An empirical study of this factor shows that its optimum value is approximately the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), require less computation time and produce less visual artifacts compared to other methods.
This work was supported by research grants from the Natural Sciences and Engineering Research Council of Canada.
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Cho, D., Bui, T.D., Chen, G. (2005). Image Denoising Using Neighbor and Level Dependency. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_12
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DOI: https://doi.org/10.1007/11559573_12
Publisher Name: Springer, Berlin, Heidelberg
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