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Image denoising via solution paths

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  • Li Wang
  • Ji Zhu

Abstract

Many image denoising methods can be characterized as minimizing “loss + penalty,” where the “loss” measures the fidelity of the denoised image to the data, and the “penalty” measures the smoothness of the denoising function. In this paper, we propose two models that use the L 1 -norm of the pixel updates as the penalty. The L 1 -norm penalty has the advantage of changing only the noisy pixels, while leaving the non-noisy pixels untouched. We derive efficient algorithms that compute entire solution paths of these L 1 -norm penalized models, which facilitate the selection of a balance between the “loss” and the “penalty.” Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Li Wang & Ji Zhu, 2010. "Image denoising via solution paths," Annals of Operations Research, Springer, vol. 174(1), pages 3-17, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:3-17:10.1007/s10479-008-0348-8
    DOI: 10.1007/s10479-008-0348-8
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Shen X. & Ye J., 2002. "Adaptive Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 210-221, March.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    2. George Chalamandaris & Nikos E. Vlachogiannakis, 2018. "Are financial ratios relevant for trading credit risk? Evidence from the CDS market," Annals of Operations Research, Springer, vol. 266(1), pages 395-440, July.

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