A simple and efficient algorithm for multifocus image fusion using morphological wavelets

De, Ishita; Chanda, Bhabatosh

doi:10.1016/j.sigpro.2005.06.015

Cited by 135 publications

(54 citation statements)

References 11 publications

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“…There exist a number of algorithms that can do this automatically (Maik et al 2007;Burt and Kolzynski 1993;De and Chanda 2006;Blum 2005). However, in our current implementation a fixed map was used to combine the images.…”

Section: Image Processing Modulementioning

confidence: 99%

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Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Tsaftaris

Noutsos

2009

Information Technologies in Environmental Engineering

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In this paper we discuss a prototype, easy-to-deploy, and low cost (~ $250) phenotype collection system for growth chambers. Off the shelf digital cameras, wireless transmitters, and PCs are used to store and process the images. A Matlab pipeline is used to fuse multiple images, identify the location of each Arabidopsis plant, segment its leaves, and measure leaf topology and area. Our early findings (unpublished) using this system for correlating genotype to phenotype are very promising. We hope that with future improvements and broad adoption, it will have the same disruptive effects as the first "build your own" microarrayers, which allowed for the explosion of genotyping information. Low cost genotyping and phenotyping will hopefully address some of the environmental, agricultural, and industrial sustainability challenges we are facing today.

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Section: Image Processing Modulementioning

confidence: 99%

“…Finally, in place of a fixed selection mask used for image fusion, a weighted map can be used to combine information from all available images. There is an abundance of publications in this field with a direct application here (Maik et al 2007;Burt and Kolzynski 1993;De and Chanda 2006;Blum 2005).…”

Section: Extensionsmentioning

confidence: 99%

Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Tsaftaris

Noutsos

2009

Information Technologies in Environmental Engineering

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“…Due to the multiresolution transform can contribute a good mathematical model of human visual system (HVS) and can provide information on the contrast changes, the multiresolution techniques have then attracted more and more interest in image fusion. Meanwhile, the multiresolution based fusion method can produce good result in less computation time using less memory [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

Yang

Huang

Gao

et al. 2014

Measurement Science Review

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In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

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“…Multifocus image fusion is one of the fusion methods that uses a set of images of the same scene captured at various focal planes of a camera to construct a clear and shape image in which all objects are focused. 19 Multifocus images comprise complementary information that can be seamlessly merged into one image more suitable for microscopic analysis of material properties, and they have been successfully used as accurate measurments of fiber diameters which could improve the fiber measurement accuracy by more than 20% 20 for nonwoven structures with respect to the fiber orientation in the nonwoven fiber. 21 In this new application for yarn hairiness measurement, the yarn passing through the microscope is intermittently advanced, and at each stop the yarn segment is imaged multiple times on different focal planes.…”

Section: Introductionmentioning

confidence: 99%

Fusing multifocus images for yarn hairiness measurement

Wang

Zhou

et al. 2014

Opt. Eng

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Abstract. Yarn hairiness has been an important indication of yarn quality that affects weaving production and fabric appearance. In addition to many dedicated instruments, various image analysis systems have been adopted to measure yarn hairiness for potential values of high accuracy and low cost. However, there is a common problem in acquiring yarn images; that is, hairy fibers protruding beyond the depth of field of the imaging system cannot be fully focused. Fuzzy fibers in the image inevitably introduce errors to the hairiness data. This paper presents a project that attempts to solve the off-focus problem of hairy fibers by applying a new imaging scheme-multifocus image fusion. This new scheme uses compensatory information in sequential images taken at the same position but different depths to construct a new image whose pixels have the highest sharpness among the sequential images. The fused image possesses clearer fiber edges, permitting more complete fiber segmentation and tracing. In the experiments, we used six yarns of different fiber contents and spinning methods to compare the hairiness measurements from the fused images with those from unfused images and from the Uster tester.

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A simple and efficient algorithm for multifocus image fusion using morphological wavelets

Cited by 135 publications

References 11 publications

Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

Fusing multifocus images for yarn hairiness measurement

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