Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique

Redondo, Rafael; Šroubek, Filip; Fischer, Sylvain; Cristóbal, Gabriel

doi:10.1016/j.inffus.2008.08.006

Cited by 109 publications

(38 citation statements)

References 35 publications

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“…Since the LGW filter has Gaussian profile, then LGW functions can provide an optimum frequency localization whose shape is smooth, symmetric and infinitely differentiable (Redondo et al 2008). The LGW transform also provide dyadic structure in frequency octaves like most of the wavelet decompositions.…”

Section: Log Gabor Waveletmentioning

confidence: 99%

Bayesian marginal statistics for speech enhancement using log Gabor wavelet

Senapati

Bhende

Saha

2011

Int J Speech Technol

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This paper deals with single-channel speech enhancement technique. Initially, the suitability of Log Gabor Wavelet (LGW) is investigated in speech enhancement approach and a novel speech enhancer by Bayesian Maximum a Posteriori (MAP) based Marginal Statistical Characterization (MSC) is developed. The LGW filters are traditional choice for obtaining localized frequency information and these offer the best simultaneous localization of time and frequency information. The MSC is applied in each scale of the LGW, that means a level dependent shrinkage rule is taken to suppress the background perturbations. The pdf of the LGW filtered speech coefficient is modeled with Generalized Laplacian Distribution (GLD), which allows a high approximation accuracy for Laplace distributed real and imaginary parts of the speech coefficients. The robustness of the proposed framework is tested on NOIZEUS speech corpus against seven different established speech enhancement algorithms. Experimental results show that the proposed estimator yield a higher improvement in Segmental SNR (S-SNR), lower Log Area Ratio (LAR) and Weighted Spectral Slope (WSS) distortion compared to existing speech enhancement algorithms.

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Section: Log Gabor Waveletmentioning

confidence: 99%

Bayesian marginal statistics for speech enhancement using log Gabor wavelet

Senapati

Bhende

Saha

2011

Int J Speech Technol

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“…The image fusion techniques comprise of three categories such as pixel level, feature level and decision level fusions [11]. The pixel level image fusion is usually employed for medical image fusion, because of easy implementation and computational efficiency [12]. Hence, it is focused in the proposed work.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Gomathi¹,

Kalaavathi²

2016

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Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, Q AB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% -40%, mean is 3% -42%, standard deviation is 1% -42%, Q AB/F is 0.4% -48% in proposed method comparing to conventional methods for six pairs of medical images.

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“…Examples of pyramid approach include the Laplacian pyramid, the contrast pyramid, the gradient pyramid, etc [13]. However, for the reason of the pyramid method fails to introduce any spatial orientation selectivity in the decomposition process, the above pyramid based methods often cause blocking effects in the fusion results [7].…”

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 using the log-Gabor transform and a Multisize Windows technique

Cited by 109 publications

References 35 publications

Bayesian marginal statistics for speech enhancement using log Gabor wavelet

Bayesian marginal statistics for speech enhancement using log Gabor wavelet

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

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

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