Fusion of 2D grayscale images using multiscale morphology

Mukhopadhyay, Susanta; Chanda, Bhabatosh

doi:10.1016/s0031-3203(00)00123-0

Cited by 94 publications

(51 citation statements)

References 15 publications

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques [10,11], morphological methods [3,4,5], discrete wavelet transform [12,13,14] and neural network fusion [15]. The different classification of image fusion involves pixel, feature and symbolic levels [16].…”

Section: Overviewmentioning

confidence: 99%

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Modeling of 3D Scene Based on Series of Photographs Taken with Different Depth-of-Field

Denkowski

Chlebiej

Mikołajczak

2008

Computational Science – ICCS 2008

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Abstract. This paper presents a method for fusing multifocus images into enhanced depth-of-field composite image and creating a 3D model of a photographed scene. A set of images of the same scene is taken from a typical digital camera with macro lenses with different depth-of-field. The method employs convolution and morphological filters to designate sharp regions in this set of images and combine them together into an image where all regions are properly focused. The presented method consists of several phases including: image registration, height map creation, image reconstruction and final 3D scene reconstruction. In result a 3D model of the photographed object is created.

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques [10,11], morphological methods [3,4,5], discrete wavelet transform [12,13,14] and neural network fusion [15]. The different classification of image fusion involves pixel, feature and symbolic levels [16].…”

Section: Overviewmentioning

confidence: 99%

“…For that purpose we propose enhanced multiscale convolution and morphology method, which we have introduced in [2]. Methods for image fusion using multiscale morphology have been broadly discussed in [3,4,5]. As an effect of fusing algorithm we obtain a height map and the reconstructed focused image with a very deep depth-offield.…”

Section: Introductionmentioning

confidence: 99%

Modeling of 3D Scene Based on Series of Photographs Taken with Different Depth-of-Field

Denkowski

Chlebiej

Mikołajczak

2008

Computational Science – ICCS 2008

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show abstract

“…Multi source image fusion is the image obtained from multiple sensors in the fusion, and it is currently a research trend. paper is intended to target recognition, discusses the fusion algorithm between optical image and SAR image, a two image multilevel mixed color fusion model is set up in order to enhance the image of information description and target recognition effect [14][15][16]28]. The paper in [29] focuses on the shadow region in the SAR image by synthetic aperture.…”

Section: Introductionmentioning

confidence: 99%

Fusion Algorithm OF Optical Images and SAR With SVT and Sparse Representation

Yin

2015

International Journal on Smart Sensing and Intelligent Systems

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques (Burt (1984); Toet (1989)), morphological methods (Ishita et al (2006); Mukopadhyay & Chanda (2001); Matsopoulos et al (1994)), discrete wavelet transform (Li et al (1995); Chibani & Houacine (2003); Lewiset al (2007)) and neural network fusion (Ajjimarangsee & Huntsberger (1988)). The different classification of image fusion involves pixel, feature and symbolic levels (Goshtasby (2007)).…”

Section: Introductionmentioning

confidence: 99%