Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization

被引：52

作者：

Purkait, Pulak ^{[1
]}

Chanda, Bhabatosh ^{[1
]}

机构：

[1] Indian Stat Inst, Commun Sci Unit, Kolkata 700108, India

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2012年 / 21卷 / 09期

关键词：

Bregman iteration; deblurring; morphologic regularization; operator splitting; subgradients; SUPERRESOLUTION RECONSTRUCTION; L(1)-MINIMIZATION; SEGMENTATION; NOISY;

D O I：

10.1109/TIP.2012.2201492

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.

引用

页码：4029 / 4039

页数：11

共 55 条

[1]

[Anonymous], SIAM J MULTISCALE MO

[2]

[Anonymous], FAST DISCRE IN PRESS

[3] EVOLUTION-EQUATIONS FOR CONTINUOUS-SCALE MORPHOLOGICAL FILTERING [J].

BROCKETT, RW ;

MARAGOS, P .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1994, 42 (12) :3377-3386

[4] LINEARIZED BREGMAN ITERATIONS FOR COMPRESSED SENSING [J].

Cai, Jian-Feng ;

Osher, Stanley ;

Shen, Zuowei .

MATHEMATICS OF COMPUTATION, 2009, 78 (267) :1515-1536

[5] Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].

Candès, EJ ;

Romberg, J ;

Tao, T .

IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509

[6]

Chandler T., 2005, 2005 International Power Engineering Conference, P1, DOI [10.1109/IPEC.2005.206895, DOI 10.1109/IPEC.2005.206895]

[7] Adaptive impulse detection using center-weighted median filters [J].

Chen, T ;

Wu, HR .

IEEE SIGNAL PROCESSING LETTERS, 2001, 8 (01) :1-3

[8]

Dai S., 2007, Computer Vision and Pattern Recognition, P1

[9] Enhancing effective depth-of-field by image fusion using mathematical morphology [J].

De, Ishita ;

Chanda, Bhabatosh ;

Chattopadhyay, Buddhajyoti .

IMAGE AND VISION COMPUTING, 2006, 24 (12) :1278-1287

[10] High probability impulse noise-removing algorithm based on mathematical morphology [J].

Deng Ze-Feng ;

Yin Zhou-Ping ;

Xiong You-Lun .

IEEE SIGNAL PROCESSING LETTERS, 2007, 14 (01) :31-34

← 1 2 3 4 5 6 →