Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

被引:214
作者
De, Ishita [1 ]
Chanda, Bhabatosh [2 ]
机构
[1] Barrackpore Rastraguru Surendranath Coll, Dept Comp Sci, Kolkata 700120, India
[2] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, India
关键词
Depth-of-field; Multi-focus image fusion; Block-based fusion; Focus measure; Energy of morphologic gradients; Quad-tree structure; ALGORITHM; SHAPE;
D O I
10.1016/j.inffus.2012.01.007
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:136 / 146
页数:11
相关论文
共 18 条
[1]
[Anonymous], 2 INT S IM SIGN PROC
[2]
Chanda B., 2000, DIGITAL IMAGE PROCES
[3]
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
[4]
Image quality measures and their performance [J].
Eskicioglu, AM ;
Fisher, PS .
IEEE TRANSACTIONS ON COMMUNICATIONS, 1995, 43 (12) :2959-2965
[5]
Multi-focus imaging using local focus estimation and mosaicking [J].
Fedorov, Dmitry ;
Sumengen, Baris ;
Manjunath, B. S. .
2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, :2093-+
[6]
Fusion of multifocus images to maximize image information [J].
Goshtasby, A. Ardeshir .
INTELLIGENT COMPUTING: THEORY AND APPLICATIONS IV, 2006, 6229
[7]
Evaluation of focus measures in multi-focus image fusion [J].
Huang, Wei ;
Jing, Zhongliang .
PATTERN RECOGNITION LETTERS, 2007, 28 (04) :493-500
[8]
MORPHOLOGICAL EDGE-DETECTION [J].
LEE, JSJ ;
HARALICK, RM ;
SHAPIRO, LG .
IEEE JOURNAL OF ROBOTICS AND AUTOMATION, 1987, 3 (02) :142-156
[9]
Li S, 2001, Information Fusion, V2, P169, DOI DOI 10.1016/S1566-2535(01)00038-0
[10]
Multifocus image fusion using region segmentation and spatial frequency [J].
Li, Shutao ;
Yang, Bin .
IMAGE AND VISION COMPUTING, 2008, 26 (07) :971-979