762 research outputs found
Survey on wavelet based image fusion techniques
Image fusion is the process of combining multiple images into a single image without distortion or loss of information. The techniques related to image fusion are broadly classified as spatial and transform domain methods. In which, the transform domain based wavelet fusion techniques are widely used in different domains like medical, space and military for the fusion of multimodality or multi-focus images. In this paper, an overview of different wavelet transform based methods and its applications for image fusion are discussed and analysed
Visible and Near Infrared Image Fusion Based on Texture Information
Multi-sensor fusion is widely used in the environment perception system of
the autonomous vehicle. It solves the interference caused by environmental
changes and makes the whole driving system safer and more reliable. In this
paper, a novel visible and near-infrared fusion method based on texture
information is proposed to enhance unstructured environmental images. It aims
at the problems of artifact, information loss and noise in traditional visible
and near infrared image fusion methods. Firstly, the structure information of
the visible image (RGB) and the near infrared image (NIR) after texture removal
is obtained by relative total variation (RTV) calculation as the base layer of
the fused image; secondly, a Bayesian classification model is established to
calculate the noise weight and the noise information and the noise information
in the visible image is adaptively filtered by joint bilateral filter; finally,
the fused image is acquired by color space conversion. The experimental results
demonstrate that the proposed algorithm can preserve the spectral
characteristics and the unique information of visible and near-infrared images
without artifacts and color distortion, and has good robustness as well as
preserving the unique texture.Comment: 10 pages,11 figure
Enhancement of Single and Composite Images Based on Contourlet Transform Approach
Image enhancement is an imperative step in almost every image processing algorithms.
Numerous image enhancement algorithms have been developed for gray scale images
despite their absence in many applications lately. This thesis proposes hew image
enhancement techniques of 8-bit single and composite digital color images. Recently, it
has become evident that wavelet transforms are not necessarily best suited for images.
Therefore, the enhancement approaches are based on a new 'true' two-dimensional
transform called contourlet transform. The proposed enhancement techniques discussed
in this thesis are developed based on the understanding of the working mechanisms of the
new multiresolution property of contourlet transform. This research also investigates the
effects of using different color space representations for color image enhancement
applications. Based on this investigation an optimal color space is selected for both single
image and composite image enhancement approaches. The objective evaluation steps
show that the new method of enhancement not only superior to the commonly used
transformation method (e.g. wavelet transform) but also to various spatial models (e.g.
histogram equalizations). The results found are encouraging and the enhancement
algorithms have proved to be more robust and reliable
Image fusion and reconstruction of compressed data: A joint approach
International audienceIn the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach
Development and implementation of image fusion algorithms based on wavelets
Image fusion is a process of blending the complementary as well as the common features of a set of images, to generate a resultant image with superior information content in terms of subjective as well as objective analysis point of view. The objective of this research work is to develop some novel image fusion algorithms and their applications in various fields such as crack detection, multi spectra sensor image fusion, medical image fusion and edge detection of multi-focus images etc. The first part of this research work deals with a novel crack detection technique based on Non-Destructive Testing (NDT) for cracks in walls suppressing the diversity and complexity of wall images. It follows different edge tracking algorithms such as Hyperbolic Tangent (HBT) filtering and canny edge detection algorithm. The second part of this research work deals with a novel edge detection approach for multi-focused images by means of complex wavelets based image fusion. An illumination invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of Dual-Tree Complex Wavelet Transform (DT-CWT) ensure robust sub band fusion. It helps in avoiding the ringing artefacts that are more pronounced in Discrete Wavelet Transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. In the third part, an improved DT-CWT based image fusion technique has been developed to compose a resultant image with better perceptual as well as quantitative image quality indices. A bilateral sharpness based weighting scheme has been implemented for the high frequency coefficients taking both gradient and its phase coherence in accoun
Novel pattern recognition methods for classification and detection in remote sensing and power generation applications
Novel pattern recognition methods for classification and detection in remote sensing and power generation application
Fusing Multiple Multiband Images
We consider the problem of fusing an arbitrary number of multiband, i.e.,
panchromatic, multispectral, or hyperspectral, images belonging to the same
scene. We use the well-known forward observation and linear mixture models with
Gaussian perturbations to formulate the maximum-likelihood estimator of the
endmember abundance matrix of the fused image. We calculate the Fisher
information matrix for this estimator and examine the conditions for the
uniqueness of the estimator. We use a vector total-variation penalty term
together with nonnegativity and sum-to-one constraints on the endmember
abundances to regularize the derived maximum-likelihood estimation problem. The
regularization facilitates exploiting the prior knowledge that natural images
are mostly composed of piecewise smooth regions with limited abrupt changes,
i.e., edges, as well as coping with potential ill-posedness of the fusion
problem. We solve the resultant convex optimization problem using the
alternating direction method of multipliers. We utilize the circular
convolution theorem in conjunction with the fast Fourier transform to alleviate
the computational complexity of the proposed algorithm. Experiments with
multiband images constructed from real hyperspectral datasets reveal the
superior performance of the proposed algorithm in comparison with the
state-of-the-art algorithms, which need to be used in tandem to fuse more than
two multiband images
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