A New Image Fusion Technique Based on Directive Contrast

Bhatnagar, Gaurav; Raman, Balasubramanian

doi:10.5565/rev/elcvia.287

Cited by 20 publications

(6 citation statements)

References 23 publications

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“…All have been done in MATLAB. The result of our approach is compared with those of the simplest pixel average-based method, NMF method [21], traditional principal component analysis (PCA) method [22], the conventional DWT method [4], and the improved DWT method [6].…”

Section: Experiments and Analysismentioning

confidence: 99%

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Image fusion via feature residual and statistical matching

Lijuan

Han

Zhang

et al. 2016

IET Computer Vision

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In view of the shortcoming of traditional image fusion based on discrete wavelet transform (DWT) with unclear textural information, an effective visible light and infrared image fusion algorithm via feature residual and statistical matching is proposed in this study. First, the source images are decomposed into low‐frequency coefficients and high‐frequency coefficients by DWT. Second, two different fusion schemes are designed for the low‐frequency coefficients and high frequency ones, respectively. The low‐frequency coefficients are fused by a local feature residual‐based scheme to achieve adaptive fusion; the high‐frequency coefficients are accomplished by a local statistical matching‐based scheme to extract the edge information effectively. Finally, the fused image is obtained by inverse DWT. Experimental results demonstrate that the proposed method can produce a more accurate fused image, leading to an improved performance compared with existing methods.

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Section: Experiments and Analysismentioning

confidence: 99%

“…Fig. 9 Comparison of visible light and infrared image fusion a Visible light image b Infrared image c Weighted average method d NMF method e Traditional PCA method f Method in[4] g Method in[6] h Method in this paper…”

mentioning

confidence: 99%

Image fusion via feature residual and statistical matching

Lijuan

Han

Zhang

et al. 2016

IET Computer Vision

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“…Thus, Focus measures are essential to differentiate focused regions from defocused regions in Multifocus image fusion to get sharper fused image. Several focus measures like contrast [17], variance [18], spatial frequency [19], and Sum-Modified-Laplacian [20] have been proposed in the literature for multifocus image fusion. The Sum-Modified-Laplacian (SML) has proven to be an effective focus measure by Huang in [20] for Multifocus image fusion.…”

Section: Introductionmentioning

confidence: 99%

Multifocus Color Image Fusion Based on Walsh Hadamard Transform and SumModified-Laplacian Focus Measure

Radha¹,

Babu²

2019

IJIES

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Multi-focus image fusion objective is to add relevant information from multiple images of the same scene but with different focuses into a sharper image that is more suitable for visual sensor networks. Natural and artificially obtained multifocus color images are considered for fusion. The existing fusion methods like multi scale and multi-resolution transforms are proved to be good in Multi-focus Image Fusion. However, they suffer from computational complexity in kernels calculation. In this paper, Multi-focus color Image Fusion based on Walsh-Hadamard Transform and sum-modified-Laplacian focus measure is proposed. Walsh-Hadamard Transform is a non-sinusoidal, orthogonal transform with symmetry, seperability and orthogonal properties. These properties make it more apt for image fusion than other transforms. And the sum-modified-Laplacian focus measure helps to get sharper image. Proposed method performance is evaluated in terms of reference and non-reference measures. The experimental results indicate that proposed method not only produces sharp details in fused image but also reduces the computational complexity.

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“…[3] Divide the HF of image 2 into 16 X 16 blocks with respect to Y direction. [4] Compute the fist order derivative of HF image with respect to X direction.…”

Section: Gradient Filter Modulementioning

confidence: 99%

“…Obtain the low frequency components and the high frequency components by using fourier transform [3] Form the low frequency and high frequency subband Coefficient of image 1,2 [4] Find the standard deviation of low frequency and high frequency subband co-efficient. [5] Obtain edge values by edge detection of image 1,2.…”

Section: Algorithm 1: Nsct Filter -----------------------------------mentioning

confidence: 99%

Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

Devi¹,

Balamurugan²

2018

IJET

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Image processing technology requires moreover the full image or the part of image which is to be processed from the user's point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edgelike features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work

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A New Image Fusion Technique Based on Directive Contrast

Cited by 20 publications

References 23 publications

Image fusion via feature residual and statistical matching

Image fusion via feature residual and statistical matching

Multifocus Color Image Fusion Based on Walsh Hadamard Transform and SumModified-Laplacian Focus Measure

Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

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