A New Multi-focus Image Fusion Method Using Principal Component Analysis in Shearlet Domain
26 Feb 2015-pp 92-98
TL;DR: A concept for multi-focus image fusion using Principal component analysis (PCA) method on shearlet domain and experimental results show that the proposed technique can tender enhanced fusion results than some existing methods in the state-of-the-art.
Abstract: The multi-focus image fusion used to produce a single image where the entire view is focused by combining multiple images taken with different focus distances. Here we present a concept for multi-focus image fusion using Principal component analysis (PCA) method on shearlet domain. Our proposed concept works on two folds, i) transform the source image into shearlet-image by using shearlet transform (ST), ii) use of PCA model in low-pass sub-band by which the best pixels in smooth parts are selected according to their arrangement. The composition of different high-pass sub-band coefficients achieved by the ST decomposition are realized. Then, the resultant fusion image is reconstructed by performing the inverse shearlet transform (IST). The experimental results, show that our proposed technique can tender enhanced fusion results than some existing methods in the state-of-the-art. This comparative assessment done in the lights of qualitative and quantitative measurements in terms of mutual information and fusion matrix QAB/F.
Citations
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TL;DR: Experimental results and comparison with other fusion techniques indicate that the proposed algorithm is fast and produces similar or better results than existing techniques for both multi-exposure as well as multi-focus images.
Abstract: A multi-exposure and multi-focus image fusion algorithm is proposed. The algorithm is developed for color images and is based on blending the gradients of the luminance components of the input images using the maximum gradient magnitude at each pixel location and then obtaining the fused luminance using a Haar wavelet-based image reconstruction technique. This image reconstruction algorithm is of O(N) complexity and includes a Poisson solver at each resolution to eliminate artifacts that may appear due to the nonconservative nature of the resulting gradient. The fused chrominance, on the other hand, is obtained as a weighted mean of the chrominance channels. The particular case of grayscale images is treated as luminance fusion. Experimental results and comparison with other fusion techniques indicate that the proposed algorithm is fast and produces similar or better results than existing techniques for both multi-exposure as well as multi-focus images.
147 citations
TL;DR: In both synthetic and real data examples, the three-dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise.
Abstract: Automatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise.
25 citations
Cites background from "A New Multi-focus Image Fusion Meth..."
...2013), image fusion (Biswas et al. 2015; Singh et al. 2015), compressed sensing (Yuan et al....
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...…including feature extraction (Rezailouyeh et al. 2013; Zhou et al. 2013; Xu, Liu and Ai 2015), noise attenuation (Fan et al. 2013), image fusion (Biswas et al. 2015; Singh et al. 2015), compressed sensing (Yuan et al. 2015), geophysical noise attenuation (Wang and Li 2013; Hosseini et al.…...
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15 Apr 2018
TL;DR: A dynamic picture mix plot for infrared and unmistakable course of action in light of range target ID is proposed in this paper, which fulfills best execution over the non-particular mix technique.
Abstract: A dynamic picture mix plot for infrared and unmistakable course of action in light of range target ID is proposed in this paper. Target acknowledgment framework is used to divide the source pictures into target and establishment areas. Particular mix rules are gotten independently in target and establishment territories. A limitedly dreary discrete wavelet change (LR DWT) methodology is familiar with finish move invariant multi-assurance depiction of each source pictures. Blend researches genuine picture progressions demonstrate that the proposed method is convincing and capable, which fulfills best execution over the non-particular mix technique.
Cites methods from "A New Multi-focus Image Fusion Meth..."
...A multi determination approach was likewise embraced in calculations created [20] and [21]....
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References
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TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Abstract: The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain multiresolution and multidirection expansion using nonseparable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and, thus, it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for N-pixel images. Furthermore, we establish a precise link between the developed filter bank and the associated continuous-domain contourlet expansion via a directional multiresolution analysis framework. We show that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing applications.
3,948 citations
"A New Multi-focus Image Fusion Meth..." refers background or methods in this paper
...??, 6 are illustrated the resultant fused images obtained from the PCA [2, 5], LPT [5], DWT [6], CT [13], NCST [14], and ST-PCA respectively....
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...In our experiments, we compared the performance of proposed ST-PCA method with .ve di.erent fusion schemes such as (1) Principal component analysis (PCA) [2, 4] (2) Laplacian pyramid technique (LPT) [5] (3) Discrete wavelet transform (DWT) [6], (4) Curvelet transform (CVT) [13] and (5) Non-sub-sampled counterlet transform (NCST) [14] respectively....
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...In our experiments, we compared the performance of proposed ST-PCA method with five different fusion schemes such as (1) Principal component analysis (PCA) [2,4] (2) Laplacian pyramid technique (LPT) [5] (3) Discrete wavelet transform (DWT) [6], (4) Curvelet transform (CVT) [13] and (5) Non-sub-sampled counterlet transform (NCST) [14] respectively....
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...However, for images contained higher dimension singularity, wavelet transform cannot achieve the optimal spare approach [13, 14]....
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...??, 6 are illustrated the resultant fused images obtained from the PCA [2,5], LPT [5], DWT [6], CT [13], NCST [14], and ST-PCA respectively....
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TL;DR: In this article, an image fusion scheme based on the wavelet transform is presented, where wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transformation of the fused wavelet coefficients.
1,532 citations
TL;DR: Experimental results clearly indicate that this metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms.
Abstract: A measure for objectively assessing the pixel level fusion performance is defined. The proposed metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms. Experimental results clearly indicate that this metric is perceptually meaningful.
1,446 citations
TL;DR: This tutorial performs a synthesis between the multiscale-decomposition-based image approach, the ARSIS concept, and a multisensor scheme based on wavelet decomposition, i.e. a multiresolution image fusion approach.
1,187 citations
TL;DR: The results show that the measure represents how much information is obtained from the input images and is meaningful and explicit.
Abstract: Mutual information is proposed as an information measure for evaluating image fusion performance. The proposed measure represents how much information is obtained from the input images. No assumption is made regarding the nature of the relation between the intensities in both input modalities. The results show that the measure is meaningful and explicit.
1,059 citations
"A New Multi-focus Image Fusion Meth..." refers background or methods in this paper
...This is accomplished by accumulating the mutual information of the fused image with each of the input images [17]....
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...Here we have selected two most popular fusion metrics such as Mutual Information (MI) [17] and Q [18], to evaluate the performance of multi-focus fusion....
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