Multispectral image fusion for improved RGB representation based on perceptual attributes

Tsagaris, V.; Anastassopoulos, V.

doi:10.1080/01431160500127609

Cited by 52 publications

(31 citation statements)

References 15 publications

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“…V.Tsagaris et.al in 2005 came up with the method based on partitioning the hyperspectral data into subgroups of bands [12]. V. Tsagaris and V. Anastassopoulos proposed Multispectral image fusion for improved RGB representation based on perceptual attributes in [13]. Q.…”

Section: Survey Of Related Researchmentioning

confidence: 99%

Colour Image Represention of Multispectral Image Fusion

Mole¹,

Mathurakani²

2016

ACIJ

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The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal-infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.

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Section: Survey Of Related Researchmentioning

confidence: 99%

Colour Image Represention of Multispectral Image Fusion

Mole¹,

Mathurakani²

2016

ACIJ

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“…In this way, noise channels (with low autocorrelation) or noise components induced by electronic or aircraft engine can be identified and eliminated [8]. A recent and adaptive linear fusion technique, which solves this same problem, is also proposed in [9]. The goal of this technique is to maximize the mutual information between the original hyperspectral bands and the fused R, G, and B channels.…”

Section: Introductionmentioning

confidence: 99%

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Mignotte

2010

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we present a nonstationary Markov random field (MRF) fusion model for the color display of hyperspectral images. The proposed fusion or dimensionality reduction model is derived from the preservation of spectral distance criterion. This quantitative metric of good dimensionality reduction and meaningful visualization allows us to derive an appealing fusion model of high-dimensional spectral data, expressed as a Gibbs distribution or a nonstationary MRF model defined on a complete graph. In this framework, we propose a computationally efficient coarse-to-fine conjugate-gradient optimization method to minimize the cost function related to this energy-based fusion model. The experiments reported in this paper demonstrate that the proposed visualization method is efficient (in terms of preservation of spectral distances and discriminality of pixels with different spectral signatures) and performs well compared to the best existing state-of-the-art multidimensional imagery color display methods recently proposed in the literature.Index Terms-Color display, complete graph, conjugategradient method, multidimensional imagery, multiresolution optimization, nonlocal Markov model, nonstationary Markov random field (MRF), visualization of hyperspectral images.

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“…A possible application is the fusion of forward looking infrared (FLIR) and low light visible images (LLTV) obtained by an airborne sensor platform to aid a pilot navigates in poor weather conditions or darkness. In pixel-level image fusion [2], some generic requirements can be imposed on the fusion result. The fusion process should preserve all relevant information of the input imagery in the composite image (pattern conservation) The fusion scheme should not introduce any artifacts or inconsistencies which would distract the human observer or following processing stages .The fusion process should be shift and rotational invariant, i.e.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Medical Image Fusion Using DWT Process on FPGA

Hussain¹,

Reddy²,

Kumar³

2013

IJCATR

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Abstract:Image fusion is a data fusion technology which keeps images as main research contents. It refers to the techniques that integrate multi-images of the same scene from multiple image sensor data or integrate multi images of the same scene at different times from one image sensor.. Wavelet Transform has good time-frequency characteristics. It was applied successfully in image processing field. Nevertheless, its excellent characteristic in one-dimension can't be extended to two dimensions or multi-dimension simply. Separable wavelet which was spanning by one-dimensional wavelet has limited directivity. The experiments show that the method could extract useful information from source images to fused images so that clear images are obtained. The selection principles about low and high frequency coefficients according to different frequency domain after wavelet. In choosing the lowfrequency coefficients, the concept of local area variance was chosen to measuring criteria. In choosing the high frequency coefficients, the window property and local characteristics of pixels were analyzed.

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Multispectral image fusion for improved RGB representation based on perceptual attributes

Cited by 52 publications

References 15 publications

Colour Image Represention of Multispectral Image Fusion

Colour Image Represention of Multispectral Image Fusion

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Implementation of Medical Image Fusion Using DWT Process on FPGA

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