Contrast-based fusion of noisy images using discrete wavelet transform

Rahman, S.M.M.; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1049/iet-ipr.2009.0163

Cited by 52 publications

(8 citation statements)

References 28 publications

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“…Since wavelet coefficients having large absolute values contain the information about the salient feature of the images such as edges and lines. Many wavelet based techniques for fusion of 2-D images have been described in the literature [3,4,5,6]. In all wavelet based image fusion schemes the wavelet transforms W of the two registered input images I 1 (x,y) and I 2 (x,y) are computed and these transforms are combined using some kind of fusion rule Φ, Figure(1).…”

Section: Wavelet Transform Fusionmentioning

confidence: 99%

Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising

PratapSinghChauhan¹,

Dwivedi²,

Negi³

2012

IJAIS

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In various real life applications such as remote sensing and medical image diagnosis, image fusion plays imperative role and it is more popular for image processing applications. Because of inadequate nature of practical imaging systems the capture images or acquired images are corrupted from various noise hence fusion of image is an integrated approach where reduction of noise and retaining the original features of image is essential. Image fusion is the process of extracting meaningful visual information from two or more images and combining them to form one fused image. Discrete Wavelet Transform (DWT) has a wide range of application in fusion of noise images. Previously, real valued wavelet transforms have been used for image fusion. Although this technique has provided improvements over more inhabitant methods, this transform suffers from the shift variance and lack of directionality associated with its wavelet bases. These problems have been overcome by the use of a reversible and discrete complex wavelet transform (the Dual Tree Complex Wavelet Transform DT-CWT). This paper therefore introduces an alternative structure such as DT-CWT that is more flexible, highly directional and shift invariant which outperforms the conventional method in terms of PSNR and image quality improvement. General TermsImage, Wavelet, Fusion of image. Image fusion rule, Denoising.

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Section: Wavelet Transform Fusionmentioning

confidence: 99%

Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising

PratapSinghChauhan¹,

Dwivedi²,

Negi³

2012

IJAIS

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show abstract

“…Many fusion algorithms have been developed in recent years, and the most popular technique is multi-resolution decomposition scheme [I], such as pyramid-based methods [2], discrete wavelet transform (DWT)-based methods [3,4,5], etc.…”

Section: Introductionmentioning

confidence: 99%

“…w pass baseband by contrast weighted sum. Rahman [5] dIVIde detail coefficients into two classes of signal dominated and noise dominated, and different weight compute rule were given to different classes. But approximate coefficients were simple fused by average.…”

Section: Introductionmentioning

confidence: 99%

Entropy based multi-resolution visible-infrared image fusion

Wang

Bao

2012

2012 IEEE Symposium on Electrical &Amp; Electronics Engineering (EEESYM)

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Image fusion is a useful technique for merging multi-sensor images to enhance the image information. Multi resolution image fusion is one class of most popular image fuse algorithms. Many attentions were given to high band coefficients fusion algorithms, but few for baseband. This paper proposed a baseband fusion algorithm based on image information entropy for multi-resolution image fusion. Baseband coefficients of fused image were obtained by weighted sum of two source images and weights for every channel are proportional to their entropy. Experiments were taken on 5 different multi-resolution image fusion algorithms based on 11 pairs of public test visual-infrared images.Experimental results show that entropy based fusion algorithms give better results than ordinary average merge or simply select one channel.

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“…The method proposed in Ref. 15, uses a zero-mean bivariate Gaussian PDF over the local neighboring detail DWT coe±cients of the input images. Moreover, Loza et al 16 have used Generalized Gaussian (GG) and alpha-stable prior to fuse noisy images.…”

mentioning

confidence: 99%

“…The innovative aspect of the present work is twofold:¯rst, we use a trivariate Gaussian PDF as in Ref. 15 for the detail 2D-SMCWT coe±cients while taking into account the mutual correlation between the input images and the fused image. A Bayesian MAP estimation technique has been used to obtain the detail 2D-SMCWT coe±cients of the fused image.…”

mentioning

confidence: 99%

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

Bengueddoudj

Messali

Mosorov

2017

J. Innov. Opt. Health Sci.

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In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT). The fusion of the detail 2D-SMCWT coe±cients is performed via a Bayesian Maximum a Posteriori (MAP) approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coe±cients. For the approximation coe±cients, a new fusion rule based on the Principal Component Analysis (PCA) is applied. We conduct several experiments using three di®erent groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against di®erent types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

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Contrast-based fusion of noisy images using discrete wavelet transform

Cited by 52 publications

References 28 publications

Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising

Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising

Entropy based multi-resolution visible-infrared image fusion

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

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