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Regularization with Multilevel Non-stationary Tight Framelets for Image Restoration
Applied and Computational Harmonic Analysis ( IF 2.5 ) Pub Date : 2021-03-23 , DOI: 10.1016/j.acha.2021.03.003
Yan-Ran Li , Raymond H.F. Chan , Lixin Shen , Xiaosheng Zhuang

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models.



中文翻译:

使用多级非平稳紧小框架进行正则化以进行图像恢复

变分正则化模型是图像恢复的流行且有效的方法之一。模型中的正则化功能带有有关要还原的图像的先验知识。尤其是对于自然图像的先验知识是一阶(即,亮度的变化)和二阶(即,对比度和纹理)信息。在本文中,我们提出了一种使用多级非平稳紧框架系统的图像恢复模型,该系统可以捕获图像的一阶和二阶信息。我们开发了一种算法来求解所提出的模型,并且数值实验表明,与其他高阶模型相比,该模型是有效且高效的。

更新日期:2021-03-24
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