Area level fusion of Multi-focused Images using Multi-Stationary Wavelet Packet Transform

Kannan, K.; Perumal, S. Arumuga; Arulmozhi, K

doi:10.5120/608-858

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…As the post-storm image is a 2D signal, the filtering and downsampling are performed first horizontally and then vertically (Kannan et al, 2010). The images used in current work are 8-bit RGB images, which can be of class uint8, uint16, single, or double.…”

Section: Edges Of Broken Buildings Extraction Using Waveletsmentioning

confidence: 99%

Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

Radhika

Tamura

Matsui

2012

Journal of Wind Engineering and Industrial Aerodynamics

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Section: Edges Of Broken Buildings Extraction Using Waveletsmentioning

confidence: 99%

Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

Radhika

Tamura

Matsui

2012

Journal of Wind Engineering and Industrial Aerodynamics

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“…Moreover, neither the scale nor the directional information of the source images is taken into consideration by using the average calculation. To overcome these problems, multiscale transform (MST) based fusion methods are proposed in [12,13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…The MST-based fusion method originates from [12], in which the source images are decomposed and reconstructed using the Laplacian Pyramid Transform (LPT). Then the Gradient Pyramid and some Wavelets, such as the Discrete Wavelet Transform (DWT) are successively used for image fusion [13,14,15,16]. However, these MST tools with limited directions cannot effectively represent the discontinuities along the edges and contours of the objects in the image [17].…”

Section: Introductionmentioning

confidence: 99%

NSCT‐based fusion method for forward‐looking sonar image mosaic

Zhang

Sohel

Bennamoun

et al. 2017

IET Radar, Sonar & Navigation

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To generate a seamless mosaic of a Forward-Looking Sonar (FLS) video sequence, this study proposes a novel fusion method for FLS image mosaic, which includes two main steps from coarse to fine. In the coarse fusion step, the source images are first decomposed into multi-scale sub-bands using the Non-Subsampled Contourlet Transform (NSCT), then the sub-bands of the source images are merged, based on the Gabor energy and the local contrast for low and high frequency sub-bands respectively, to generate the fused sub-bands which are used to produce the fused image by inverse NSCT. In the fine fusion step, a decision map is used to choose the pixels of the fine fused image from the coarsely fused image or one of the source images. This decision map is first constructed by measuring the similarity of the coarsely fused image to the source images, and then processed by a morphological post-processing technique to ensure its continuity and smoothness. Extensive experiments on FLS image fusion and mosaic have been conducted to demonstrate the effectiveness and the superiority of the proposed technique using both a subjective evaluation and objective metrics.

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“…Morphological operations were performed in [ 22 ], in addition to the wavelet transform, to obtain the fused image. In [ 23 ], the problem of single image dehazing is addressed via a Laplacian pyramid-based multi-scale approach, while wavelet packets, operating in both the spatial, as well as the transform domain, were used in [ 24 ]. In [ 25 ], undecimated wavelet transform along with nonorthogonal filter banks were utilised in a multi-scale fusion approach.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

Rehman

Ehsan

Abdullah³

et al. 2015

Sensors

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A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

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Area level fusion of Multi-focused Images using Multi-Stationary Wavelet Packet Transform

Abstract: Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content.

Cited by 10 publications

References 14 publications

Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

NSCT‐based fusion method for forward‐looking sonar image mosaic

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

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