A new look at IHS-like image fusion methods

Tu, Te-Ming; Su, Shun-Chi; Shyu, Hsuen-Chyun; Huang, Ping

doi:10.1016/s1566-2535(01)00036-7

Cited by 797 publications

(414 citation statements)

References 6 publications

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“…Clearly, the more correlated the PAN is with the replaced component, the less distortion is introduced. A simple and fast procedure is based on the intensity-hue-saturation (IHS) transform [4], which can be used only when three bands are available. However, a generalized IHS transform (GIHS) can be defined [5] which includes the response of the near-infrared (NIR) band.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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Abstract:A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

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Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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“…The disintegration of image into a range of levels is completed by employing low pass filters which recursively filters the low level image. As the level increases the image becomes small by preceding the length of the image [7].…”

Section: Laplacian Pyramidmentioning

confidence: 99%

“…Te-Ming Tu et al [7] had shown that the modern image fusion techniques, that were IHS, PCA, BT and WT, face color distortion had troubled with fused images. In this paper it was examined that the color distortion problem occurred due to the change of saturation throughout the fusion practice.…”

Section: Behzad Kalafje Nobariyan Et Al [4]mentioning

confidence: 99%

A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

Kaur¹,

Kaur²

2018

IJCA

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Image fusion procedure is helpful to attain a single output image that contains required data or information by merging two distorted input images. Image fusion is done by pulling out all the necessary information from two images or more than two images after which the extorted information is combined into a distinct fused image. This fused image has improved superiority as compare to the input images. Image fusion is prepared by implementing particular techniques. Those particular schemes for image fusion are introduced in this paper. This paper offers an overview to the work that has been done by in past.

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“…The "Amélioration de la Résolution Spatiale par Injection de Structures" (ARSIS, improving spatial resolution by structure injection) concept is based on the assumption that the missing information is linked to the high frequencies of the datasets to be fused [5]. PCA, Brovey and wavelet transform fusion techniques are evaluated and found to be IHS-like image merging techniques [75]. A comprehensive framework, the general image fusion (GIF) method, makes it possible to categorize, compare, and evaluate the existing image fusion methods [76].…”

Section: A Short Overview Of Existing Pan-sharpening Algorithmsmentioning

confidence: 99%

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

2014

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Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions.

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A new look at IHS-like image fusion methods

Cited by 797 publications

References 6 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

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