基于张量扩散的小波域修复模型

杨秀红; 郭宝龙; 吴宪祥

doi:10.3724/SP.J.1004.2013.01071

基于张量扩散的小波域修复模型

doi: 10.3724/SP.J.1004.2013.01071

1.
西安电子科技大学ICIE研究所西安 710071

基金项目:

Supported by National Natural Science Foundation of China (61003196, 61105066) and the Fundamental Research Funds for the Central Universities (K50510040007)

详细信息

通讯作者:
杨秀红

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- 被引次数: 0
出版历程
- 收稿日期: 2012-09-05
- 修回日期: 2013-01-09
- 刊出日期: 2013-07-20

Wavelet Inpainting Based on Tensor Diffusion

1.
ICIE Institute, School of Electromechanical Engineering, Xidian University, Xi'an 710071, China

Funds:

Supported by National Natural Science Foundation of China (61003196, 61105066) and the Fundamental Research Funds for the Central Universities (K50510040007)

摘要

摘要: 在JPEG2000图像压缩标准中,有损传输过程中的小波系数的丢失将严重影响接收端图像的质量.为了修复丢失的或被损坏的小波系数,本文提出了一种基于张量扩散的小波域修复模型(TDWI),该混合模型将结构自适应各向异性正则与小波表示结合起来.同时推导该模型对应的Euler-Lagrange方程,并据此来分析它在像素域的几何正则性能.由于在正则项中采用了矩阵值的结构张量,该模型的扩散核的形状随着图像的局部结构特征(包括尖锐边缘、角点和各向同性区域)自适应地变化.与已有的小波域修复模型相比,本文所提模型能更自适应地、更准确地控制像素域的几何正则性,并对噪声有更强的鲁棒性.另外,本文采用了一个更加有效且适合的数值实现方法来进一步改善所提模型的修复性能.最后,给出了各种丢失情形下的实验结果来表明该模型在小波域修复性能和抗噪性能等方面的优越性.
- 小波 /
- 修复 /
- 结构张量 /
- 扩散 /
- 各向异性 /
- 正则
Abstract: Due to the lossy transmission in the JPEG2000 image compression standard, the loss of wavelet coefficients heavily affects the quality of the received image. In this paper, we propose a novel wavelet inpainting model based on tensor diffusion (TDWI) to restore the missing or damaged wavelet coefficients. A hybrid model is built by combining structure-adaptive anisotropic regularization with wavelet representation. Its associated Euler-Lagrange equation is also given for analyzing its regularity performance. Owing to the matrix representation of the structure tensor in the regularization term, the shape of diffusion kernel changes adaptively according to the image features, including sharp edges, corners and homogeneous regions. Compared with existing wavelet inpainting models, the proposed one can control more adaptively and accurately the geometric regularity in the image and exhibits better robustness to noise. In addition, an effective and proper numerical scheme is adopted to improve the computation. Experimental results on a variety of loss scenarios are given to demonstrate the advantages of our proposed model.
- Wavelet /
- inpainting /
- structure tensor /
- diffusion /
- anisotropic /
- regularization

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