Abstract
Ringing and noise amplification are the most dominant artifacts in image deconvolution. These artifacts can be reduced by introducing image prior into the deconvolution process. A regularization weighting factor can control the strength of regularization. Ringing and noise can be reduced significantly with the strong weighting factor, but details can be lost. We propose a non-blind image deconvolution method with adaptive regularization that can reduce ringing and noise in the smooth region and preserve image details in the textured region simultaneously. For adaptive regularization, we make a reference image that gives proper edge information and helps to restore a latent image. The reference image guides the strength of the weighting factor on the pixel of the blurred image. Experimental results show that ringing and noise are suppressed efficiently, while preserving image details.
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Lee, JH., Ho, YS. (2010). Non-blind Image Deconvolution with Adaptive Regularization. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_66
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DOI: https://doi.org/10.1007/978-3-642-15702-8_66
Publisher Name: Springer, Berlin, Heidelberg
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