Compound tetrolet sparsity and total variation regularization for image restoration

Liqian Wang; Liang Xiao; Zhihui Wei

doi:10.1117/12.911829

14 December 2011 Compound tetrolet sparsity and total variation regularization for image restoration

Liqian Wang, Liang Xiao, Zhihui Wei

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 80021T (2011) https://doi.org/10.1117/12.911829
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Image restoration is one of the most classical problems in image processing. The main issue of image restoration is deblurring as well as preserving the fine details. In order to restore the high quality image, we propose a compound regularization method which combines the tetrolet-based sparsity and a new weighted adaptive total variation (ATV). Tetrolet transform is a geometric adaptive Haar-type wavelet transform. It finds the optimal partition to fit the local image structures and the tetrolet coefficients can capture the textures and details information in different image scales. ATV adds two directional gradient operators into the original anisotropic TV. It not only seeks the intensity continuity horizontally and vertically, but also seeks the intensity continuity diagonally. Combining the tetrolet-based sparsity and ATV together, our model can restore the local structures and details by the tetrolet-based sparsity regularization while suppress the noise and recover piecewise smooth images with sharp edges along four directions by the ATV regularization. For solving the minimizing problem, we propose an algorithm which consists of the variable splitting method and the Dual Douglas-Rachford splitting method. The Experimental results demonstrate the efficiency of our image restoration method for preserving the structure details and the sharp edges of image.

Citation Download Citation

Liqian Wang, Liang Xiao, and Zhihui Wei "Compound tetrolet sparsity and total variation regularization for image restoration", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80021T (14 December 2011); https://doi.org/10.1117/12.911829

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available