A new quality metric for image fusion

Piella, Gemma; Heijmans, H.J.A.M.

doi:10.1109/icip.2003.1247209

Cited by 651 publications

(452 citation statements)

References 5 publications

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“…The enhanced images will be compared with the ground truth image, in terms of Peak Signal-toNoise Ratio (PSNR) and Image Quality Index Q 0 , as proposed by Wang and Bovik [17]. In these experiments, the fusion indexes proposed by Petrovic and Xydeas [18] and Piella [15], cannot be used since they measure the amount of information that has been transferred from the input images to the fused image. Since the proposed fusion-restoration approach aims at enhancing the areas that have low quality information in the input images, it makes no sense to use any evaluation approach that employs the input images as a comparison standard.…”

Section: Examples Of Joint Image Fusion and Restorationmentioning

confidence: 99%

“…the hidden man in the middle of the picture. Consequently, one should also consult the human operators of modern fusion systems, apart from proposed fusion metrics [15,18], in order to evaluate efficiently the performance of these algorithms. Perhaps the outlined fusion result is more appealing to human operators and the human vision system in general and therefore may be also be examined as a preferred solution.…”

Section: Fusion Experiments Of Out-of-focus and Multimodal Image Setsmentioning

confidence: 99%

“…Most test images were taken from the Image Fusion server [3]. The numerical evaluation in most experiments was performed using the indexes proposed by Piella [15] and Petrovic [18].…”

Section: Fusion Experiments Of Out-of-focus and Multimodal Image Setsmentioning

confidence: 99%

See 2 more Smart Citations

Enhancement of multiple sensor images using joint image fusion and blind restoration

Mitianoudis

Stathaki

2008

Image Fusion

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Image fusion systems aim at transferring "interesting" information from the input sensor images to the fused image. The common assumption for most fusion approaches is the existence of a high-quality reference image signal for all image parts in all input sensor images. In the case that there are common degraded areas in at least one of the input images, the fusion algorithms can not improve the information provided there, but simply convey a combination of this degraded information to the output. In this study, the authors propose a combined spatial-domain method of fusion and restoration in order to identify these common degraded areas in the fused image and use a regularised restoration approach to enhance the content in these areas. The proposed approach was tested on both multi-focus and multi-modal image sets and produced interesting results.

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Section: Examples Of Joint Image Fusion and Restorationmentioning

confidence: 99%

Section: Fusion Experiments Of Out-of-focus and Multimodal Image Setsmentioning

confidence: 99%

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Enhancement of multiple sensor images using joint image fusion and blind restoration

Mitianoudis

Stathaki

2008

Image Fusion

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“…For two spatial locations x and y, the SSIM is defined as [2,7]: (1) or (2) where are weights on the luminance , contrast and structure terms. They describe the fidelity measure.…”

Section: The Ssimmentioning

confidence: 99%

“…The ultimate goal of image quality assessment is the evaluation of the structural fidelity of a processed image. The SSIM index has found many applications from image classification [3][4][5][6], restoration and fusion [7], to watermarking, denoising and biometrics [8]. It is well known that noise in images has various sources.…”

Section: Introductionmentioning

confidence: 99%

Structural similarity analysis for brain MR image quality assessment

Punga¹,

Moldovanu²,

Moraru³

2014

AIP Conference Proceedings

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Abstract. Brain MR images are affected and distorted by various artifacts as noise, blur, blotching, down sampling or compression and as well by inhomogeneity. Usually, the performance of pre-processing operation is quantified by using the quality metrics as mean squared error and its related metrics such as peak signal to noise ratio, root mean squared error and signal to noise ratio. The main drawback of these metrics is that they fail to take the structural fidelity of the image into account. For this reason, we addressed to investigate the structural changes related to the luminance and contrast variation (as non-structural distortions) and to denoising process (as structural distortion)through an alternative metric based on structural changes in order to obtain the best image quality.

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An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

Wang

Yang

2020

J Applied Clin Med Phys

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In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two-stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal-dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR-T1\MR-T2 images, and the comparison metrics SF, MI, Q AB=F , Q W , and VIFF are calculated for assessment. Compared with seven state-of-the-art methods, the proposed method shows a comprehensive advantage in preserving the salient intensity features, as well as texture structure information, not only in visual effects but also in objective assessments.

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A new quality metric for image fusion

Cited by 651 publications

References 5 publications

Enhancement of multiple sensor images using joint image fusion and blind restoration

Enhancement of multiple sensor images using joint image fusion and blind restoration

Structural similarity analysis for brain MR image quality assessment

An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

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