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Abstract
Fusing information contained in multiple images plays an increasingly important role for
quality inspection in industrial processes as well as in situation assessment for
autonomous systems and assistance systems. The aim of image fusion in general is to use
images as redundant or complementary sources to extract information from them with
higher accuracy or reliability. This dissertation describes image fusion in detail, and
firstly introduces the three basic levels which are pixel level, feature level and decision
level fusion, and then compares with their properties and all other aspects. Then it
describes the evaluation criteria of image fusion results from subjective evaluation and
objective evaluation two aspects. According to the quantitative evaluation of the image
fusion results and quality, this text uses and defines multiple evaluation parameters such
as fusion image entropy, mutual information MI, the average gradient, standard
deviation, cross-entropy, unite entropy, bias, relative bias, mean square error, root mean
square error and peak SNR, and establishes the corresponding evaluation criteria
Keywords: - image fusion, wavelet transform, DCT, neural network, Genetic algorithm
Introduction
With the continuous development of sensor technology, people have more and more
ways to obtain images, and the image fusion types are also increasingly rich, such as the
Image fusion of same sensor, the multi-spectral image fusion of single-sensor, the image
fusion of the sensors with different types, and the fusion of image and non-image.
Traditional data fusion can be divided into three levels, which are pixel-level fusion,
feature-level fusion and decision-level fusion. The different fusion levels use differentfusion algorithms and have different applications, generally, we all research the pixel-
level fusion. Classical fusion algorithms include computing the average pixel-pixel gray
level value of the source images, Laplacian pyramid, Contrast pyramid, Ratio pyramid,
and Discrete Wavelet Transform (DWT). However, computing the average pixel-pixel
gray level value of the source images method leads to undesirable side effects such as
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contrast reduction. The basic idea of DWT based methods is to perform decompositions
on each source image, and then combine all these decompositions to obtain composite
representation, from which the fused image can be recovered by finding inverse
transform. This method is shown to be effective. However, wavelets transform can only
reflect "through" edge characteristics, but can not express "along" edge characteristics. At
the same time, the wavelet transform cannot precisely show the edge direction since it
adopts isotropy. According to the limitation of the wavelet transform, Donoho et al. was
proposed the concept of Curvelet transform, which uses edges as basic elements,
possesses maturity, and can adapt well to the image characteristics. Moreover, Curvelet
Transform has anisotropy and has better direction, can provide more information to
image processing [1-2]. Through the principle of Curvelet transform we know that:
Curvelet transform has direction characteristic, and its base supporting session satisfies
content anisotropy relation, except have multi-scale wavelet transform and local
characteristics. Curvelet transform can represent appropriately the edge of image and
smoothness area in the same precision of inverse transform. The low-bands coefficient
adopts NGMS method and different direction high-bands coefficient adopts LREMS
method was proposed after researching on fusion algorithms of the low-bands coefficient
and high-bands coefficient in Curvelet transform
Figure 1 process of image fusion algorithm base on Curvelet transform Fusion methods
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The following summarize several approaches to the pixel level fusion of spatially
registered input images. Most of these methods have been developed for the fusion of
stationary input images (such as multispectral satellite imagery). Due to the static nature
of the input data, temporal aspects arising in the fusion process of image sequences, e.g.
stability and consistency, are not addressed.
A generic categorization of image fusion methods is the following:
linear superposition
nonlinear methods
optimization approaches
artificial neural networks
image pyramids
wavelet transform
generic multiresolution fusion scheme
Linear Superposition
The probably most straightforward way to build a fused image of several input
frames is performing the fusion as a weighted superposition of all input frames. The
optimal weighting coefficients, with respect to information content and redundancy
removal, can be determined by a principal component analysis (PCA) of all input
intensities. By performing a PCA of the covariance matrix of input intensities, the
weightings for each input frame are obtained from the eigenvector corresponding to the
largest eigenvalue. A similar procedure is the linear combination of all inputs in a pre-
chosen colorspace (eg. R-G-B or H-S-V), leading to a false color representation of the
fused image.
Nonlinear Methods
Another simple approach to image fusion is to build the fused image by the
application of a simple nonlinear operator such as max or min. If in all input images the
bright objects are of interest, a good choice is to compute the fused image by an pixel-by-
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pixel application of the maximum operator. An extension to this approach follows by the
introduction of morphological operators such as opening or closing. One application is
the use of conditional morphological operators by the definition of highly reliable 'core'
features present in both images and a set of 'potential' features present only in one source,
where the actual fusion process is performed by the application of conditional erosion
and dilation operators. A further extension to this approach is image algebra, which is a
high-level algebraic extension of image morphology, designed to describe all image
processing operations. The basic types defined in image algebra are value sets, coordinate
sets which allow the integration of different resolutions and tessellations, images and
templates. For each basic type binary and unary operations are defined which reach from
the basic set operations to more complex ones for the operations on images and
templates. Image algebra has been used in a generic way to combine multisensor images
Optimization Approaches
In this approach to image fusion, the fusion task is expressed as an bayesian
optimization problem. Using the multisensor image data and an a-prori model of the
fusion result, the goal is to find the fused image which maximizes the a-posteriori
probability. Due to the fact that this problem cannot be solved in general, some
simplifications are introduced: All input images are modeled as markov random fields to
define an energy function which describes the fusion goal. Due to the equivalence of of
gibbs random fields and markov random fields, this energy function can be expressed as
a sum of so-called clique potentials, where only pixels in a predefined neighborhood
affect the actual pixel. The fusion task then consists of a maximization of the energy
function. Since this energy function will be non-convex in general, typically stochastic
optimization procedures such as simulated annealing or modifications like iterated
conditional modes will be used.
Image Pyramids
Image pyramids have been initially described for multiresolution image analysis
and as a model for the binocular fusion in human vision. A generic image pyramid is a
sequence of images where each image is constructed by low pass filtering and sub
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sampling from its predecessor. Due to sampling, the image size is halved in both spatial
directions at each level of the decomposition process, thus leading to an multiresolution
signal representation. The difference between the input image and the filtered image is
necessary to allow an exact reconstruction from the pyramidal representation. The image
pyramid approach thus leads to a signal representation with two pyramids: The
smoothing pyramid containing the averaged pixel values, and the difference pyramid
containing the pixel differences, i.e. the edges. So the difference pyramid can be viewed
as a multiresolution edge representation of the input image.
The actual fusion process can be described by a generic multiresolution fusion
scheme which is applicable both to image pyramids and the wavelet approach. There are
several modifications of this generic pyramid construction method described above.
Some authors propose the computation of nonlinear pyramids, such as the ratio and
contrast pyramid, where the multistage edge representation is computed by an pixel-by-
pixel division of neighboring resolutions. A further modification is to substitute the linear
filters by morphological nonlinear filters, resulting in the morphological pyramid.
Another type of image pyramid - the gradient pyramid - results, if the input image is
decomposed into its directional edge representation using directional derivative filter
Wavelet Transform
A signal analysis method similar to image pyramids is the discrete wavelet
transform. The main difference is that while image pyramids lead to an over complete set
of transform coefficients, the wavelet transform results in a nonredundant image
representation. The discrete 2-dim wavelet transform is computed by the recursive
application of lowpass and high pass filters in each direction of the input image (i.e. rows
and columns) followed by sub sampling. Details on this scheme can be found in the
reference section. One major drawback of the wavelet transform when applied to imagefusion is its well known shift dependency, i.e. a simple shift of the input signal may lead
to complete different transform coefficients. This results in inconsistent fused images
when invoked in image sequence fusion. To overcome the shift dependency of the
wavelet fusion scheme, the input images must be decomposed into a shift invariant
representation. There are several ways to achieve this: The straightforward way is to
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compute the wavelet transform for all possible circular shifts of the input signal. In this
case, not all shifts are necessary and it is possible to develop an efficient computation
scheme for the resulting wavelet representation. Another simple approach is to drop the
subsampling in the decomposition process and instead modify the filters at each
decomposition level, resulting in a highly redundant signal representation.
The actual fusion process can be described by a generic multiresolution fusion
scheme which is applicable both to image pyramids and the wavelet approach.
Generic Multiresolution Fusion Scheme
The basic idea of the generic multiresolution fusion scheme is motivated by the
fact that the human visual system is primary sensitive to local contrast changes, i.e.
edges. Motivated from this insight, and in mind that both image pyramids and the wavelet
transform result in an multiresolution edge representation, it is straightforward to build
the fused image as a fused multiscale edge representation. The fusion process is
summarized in the following: In the first step the input images are decomposed into their
multiscale edge representation, using either any image pyramid or any wavelet transform.
The actual fusion process takes place in the difference resp. wavelet domain, where the
fused multiscale representation is built by a pixel-by-pixel selection of the coefficients
with maximum magnitude. Finally the fused image is computed by an application of the
appropriate reconstruction scheme
Fig. 2 Block Diagram Of Basic Image Fusion Proces
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Related work (Survey)
Low frequency coefficient fusion algorithm
Curvelet transform is close to wavelet transform in low frequency region, image
component including main energy decide image contour, so it can enhance effect of the
image vision by correctly selecting low frequency coefficient. Existing fusion rule mostly
have max pixel method, min pixel method, computing the average pixel-pixel gray level
value of the source images method,LREMSmethod, local region deviation method [6].
Max pixel method, min pixel method and computing the average pixel-pixel gray level
value of the source images method did not take into account local neighbor relativity each
other, so fusion result can not get better effect; local region energy method and deviation
method onside take into account local neighbor relativity each other, but did not take into
account image edge and definition. Accounting to this lack, NGMSmethod, it mainly
describes image detail and image in focus grade. Eight local neighbor relativity sum of
Laplacian algorithm was adopted to evaluate of Image definition, it defines as [9]:
..(1)
High frequency coefficient fusion algorithm
Curvelet transform have excessive direction characteristics, so can precisely express
image edge orientation, and that region of high frequency coefficient namely express
image edge detail information. Pixel absolute max method, LREMSmethod, local region
deviation method, direction contrast method etc. was used in high frequency coefficient.
Hypothesis image high frequency coefficient is CH, then fusion algorithm such as:
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(2)
Where CHA and CHB express Curvelet transform high frequency coefficient of image A
and image B, CHF(x, y) show high frequency coefficient in pot(x, y) fusion high
frequency coefficient, ECHA (x, y) show Curvelet transform high frequency coefficient
of image A in pot(x, y) local region energy, ECHB (x,y) show Curvelet transform high
frequency coefficient of image B in pot(x, y) local region energy
Image fusion different levels
Pixel-level fusion
Pixel-level fusion is to fuse on the raw data layer with strict registration conditions, and
carry out data integration and analysis before the raw data of various sensors being
pre-processed. Pixel-level image fusion is the lowest level of image fusion, which is to
keep more raw data as much as possible to provide rich and accurate image information
other fusion levels can not provide, so that the image will be easy to be analyzed and
processed, such as image fusion, image segmentation and feature extraction, etc.,
Pixel-level image fusion structure as shown in figure 2:
The images to participate the fusion may come from multiple image sensors with
different types, also may from a single image sensor. The various images the single
image sensor provided may come from different observation time or space (perspective),
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also may be the image with different spectral characteristics in the same time or space.
The image after the pixel-level image fusion contains much richer, more accurate
information content, which is conducive to the analysis and processing of image signal,
makes it easier for people observation and more suitable for computer detection
processing, it is the most important and the most fundamental multi-sensor image fusion
method. Pixel-level image fusion advantage is a minimum loss of information, but it has
the largest amount of information to be processed, the slowest processing speed, and a
higher demand for equipment
Feature-level fusion
Feature-level fusion is intermediate level, it is to carry out feature extraction (features can
be the goal edges, direction, speed, etc.) for the original information of the various
sensors, and then comprehensively analyze and process the feature information. As
shown in figure
In general, the extracted feature information should be a sufficient statistic of the pixel
information, and then multisensor data will be classified, collected and integrated
according to the feature information. If the data the sensor obtained is image data, then
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the feature is abstractly extracted from the image pixel information, and the typical
feature information has cable type, edge, texture, spectrum, similar brightness area,
similar depth of field areas, etc., and then multi-sensor image feature integration and
classification will be achieved. Feature-level fusion advantage is that it achieved
considerable compression of information, is conducive to real-time processing, and its
fusion results can furthest give the feature information the decision analysis needed,
which is because that the extracted features are directly related to the decision analysis
Improved ihs-based fusion
The basic idea of IHS fusion method is to convert a color image from the RGB (Red,
Green, Blue) color space into the IHS (Intensity, Hue, Saturation) color space. One of
them will be replaced by another image when we got the intensive information of both
images. Then we convert IHS color space with H and S of being replaced image into
RGB color space. See the following procedure
Step1: Transform the color space from RGB to IHS.
(3)
where Iv is intensity of visual image. R,G, B is color information of visual image
respectively. 1 Vand 2 Vare components to calculate hueHand saturation S
Step 2: The intensity component is replaced by intensity of infrared imageIi .Step 3: Transform the color space from IHS to RGB.
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(4)
where Ii is intensity of infrared image. R',G',B' is color information of fused image
respectively. Because our basic idea is to add useful information of far infrared image to
visual image. We set fused parameters in the matrix instead of the intensity of far infrared
image Ii to replace the intensity of visual image Iv . The fused parameters will be
adjusted according different information of each region. The following formula ismodified result
(5)where , are fused parameters. 0 , 1.
Artificial neural network
Artificial neural network (ANN) has good advantage to estimate the relation between
input and output when we could not know the relation of input and output, especially the
relation is nonlinear. Generally speaking, ANN is divided into two parts. One is training,
another is testing. During the training, we have to define training data and relational
parameters. In the testing, we have to define testing data then get fused parameters. It has
good ability to learn from examples and extract the statistical properties of the examples
during the training procedure. Feature extraction is the important pre-procedure for ANN.
In our case, we choice four feature, respectively, average intensity of visual image Mv ,
average intensity of infrared image Mi , average intensity of region in infrared image Mir
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and visibility Vi to present as input of ANN. The following is our introduction of
features. The average intensity of visual image Mv :
..(6)
where fv is visual gray image, Hand Ware height and width of visual image Generally
speaking, it possible means the content of the image is shot in the daytime when Mv is
larger. On the other hand, the content of the image is shot in the night. But it is initial
assumption, not accurate. The average intensity ofMi is defined as follow:
where fi is infrared image, H and W are height and width of visual image. Generally
speaking, it possible means the content of the image was shot in the daytime when Mi is
larger. On the other hand, the content of the image was shot in the night. If we consider
Mv and Mi to assume the shot night when Mv and Mi both are larger or smaller
respectively. IfMi is larger and Mv is smaller then we can suppose that the highlight of
infrared image could be useful information for us. IfMi is smaller and Mv is larger then
we can suppose that it could be no useful information in the infrared image to add to
visual image. The average intensity of region in Miris defined as follow We can start to
define the training data and testing data when getting the four features. The Fig. 2 is one
of our training data, they are visual image, infrared image and segmented infrared image
respectively from left to right. We only segment the infrared image here. And we use
color depth to represent each region. There are five level to represent five region. Table I
is the integration of the features of each region from segmented infrared image. Each
region from 1 to 5 is the color level from deep to shallow respectively. One region has
four features
Proposed Technique for Image Fusion
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Various method are available for image fusion such as wavelet DCT ,WCT trained pixel
process using neural network but all these are not efficient for the proper matching of
pixel on the time of property matching . Now we have proposed new method for image
fusion using the Meta heuristic function genetic algorithm. Genetic algorithm is a
heuristic function that function used for the purpose for optimization of produced result
by any process or algorithm.
Genetic algorithm
Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the
evolutionary ideas of natural selection and genetics. As such they represent an intelligent
exploitation of a random search used to solve optimization problems. Althoughrandomised, GAs are by no means random, instead they exploit historical information to
direct the search into the region of better performance within the search space. The basic
techniques of the GAs are designed to simulate processes in natural systems necessary for
evolution, specially those follow the principles first laid down by Charles Darwin of
"survival of the fittest.". Since in nature, competition among individuals for scanty
resources results in the fittest individuals dominating over the weaker ones. It is better
than conventional AI in that it is more robust. Unlike older AI systems, they do not break
easily even if the inputs changed slightly, or in the presence of reasonable noise. Also, in
searching a large state-space, multi-modal state-space, or n-dimensional surface, a
genetic algorithm may offer significant benefits over more typical search of optimization
techniques. (linear programming, heuristic, depth-first, breath-first, and praxis.) GAs
simulate the survival of the fittest among individuals over consecutive generation for
solving a problem. Each generation consists of a population of character strings that are
analogous to the chromosome that we see in our DNA. Each individual represents a point
in a search space and a possible solution. The individuals in the population are then made
to go through a process of evolution.GAs are based on an analogy with the genetic
structure and behaviour of chromosomes within a population of individuals using the
following foundations:
Individuals in a population compete for resources and mates.
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Those individuals most successful in each 'competition' will produce more
offspring than those individuals that perform poorly.
Genes from `good' individuals propagate throughout the population so that two
good parents will sometimes produce offspring that are better than either parent.
Thus each successive generation will become more suited to their environment.
Search Space
A population of individuals are is maintained within search space for a GA, each
representing a possible solution to a given problem. Each individual is coded as a finite
length vector of components, or variables, in terms of some alphabet, usually the binary
alphabet {0,1}. To continue the genetic analogy these individuals are likened to
chromosomes and the variables are analogous to genes. Thus a chromosome (solution) is
composed of several genes (variables). A fitness score is assigned to each solution
representing the abilities of an individual to `compete'. The individual with the optimal
(or generally near optimal) fitness score is sought. The GA aims to use selective
`breeding' of the solutions to produce `offspring' better than the parents by combining
information from the chromosomes. The GA maintains a population of n chromosomes
(solutions) with associated fitness values. Parents are selected to mate, on the basis of
their fitness, producing offspring via a reproductive plan. Consequently highly fitsolutions are given more opportunities to reproduce, so that offspring inherit
characteristics from each parent. As parents mate and produce offspring, room must be
made for the new arrivals since the population is kept at a static size. Individuals in the
population die and are replaced by the new solutions, eventually creating a new
generation once all mating opportunities in the old population have been exhausted. In
this way it is hoped that over successive generations better solutions will thrive while the
least fit solutions die out. New generations of solutions are produced containing, on
average, more good genes than a typical solution in a previous generation. Each
successive generation will contain more good `partial solutions' than previous
generations. Eventually, once the population has converged and is not producing
offspring noticeably different from those in previous generations, the algorithm itself is
said to have converged to a set of solutions to the problem at hand.
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Based on Natural Selection
After an initial population is randomly generated, the algorithm evolves the through three
operators:
1. selection which equates to survival of the fittest;
2. crossover which represents mating between individuals;
3. mutation which introduces random modifications.
1. Selection Operator
key idea: give prefrence to better individuals, allowing them to pass on their genes
to the next generation.
The goodness of each individual depends on its fitness.
Fitness may be determined by an objective function or by a subjective judgement.
2. CrossoverOperator
Prime distinguished factor of GA from other optimization techniques
Two individuals are chosen from the population using the selection operator
A crossover site along the bit strings is randomly chosen
The values of the two strings are exchanged up to this point
If S1=000000 and s2=111111 and the crossover point is 2 then S1'=110000 and
s2'=001111
The two new offspring created from this mating are put into the next generation of
the population
By recombining portions of good individuals, this process is likely to create even
better individuals
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3. Mutation Operator
With some low probability, a portion of the new individuals will have some of
their bits flipped.
Its purpose is to maintain diversity within the population and inhibit premature
convergence.
Mutation alone induces a random walk through the search space
Mutation and selection (without crossover) create a parallel, noise-tolerant, hill-
climbing algorithms
Effects of Genetic Operators
Using selection alone will tend to fill the population with copies of the best
individual from the population
Using selection and crossover operators will tend to cause the algorithms to
converge on a good but sub-optimal solution Using mutation alone induces a random walk through the search space.
Using selection and mutation creates a parrallel, noise-tolerant, hill climbing
algorithm
The Algorithms
1. randomly initialize population(t)
2. determine fitness of population(t)3. repeat
1. select parents from population(t)
2. perform crossover on parents creating population(t+1)
3. perform mutation of population(t+1)
4. determine fitness of population(t+1)
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Conclusion
This dissertation describes an application of genetic algorithm to image fusion problem.
We improve traditional IHS-method, wavelet, NN method and pattern matching method
and add concept of region-based into image fusion. The aim is that different regions can
be used by different parameters in different state about time or weather. Due to the
relation between environment and fused Parameters are nonlinear. So, we adopt artificial
neural network to solve this problem. On the other hand, the fused parameters will be
estimated automatically render us to get adaptive appearance in different states. The
architecture we proposed is not only can be useful for many applications but also adapted
for many kinds of field. In the next semester we have implemented this entire concept inmatlab.
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REFERENCES
[1] Z. Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, A Comparative Analysis of
Image Fusion Methods, Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1391-1402,June 2006.
[2] J. G. Liu, Smoothing filter-based intensity modulation: A spectral preserve image
fusion technique for improving spatial details, Int. J. Remote Sensing, vol. 21, no. 18,
pp. 3461-3472, 2000.
[3] M. Li, W. Cai, and Z. Tan, A region-based multi-sensor image fusion scheme using
pulse-coupled neural network, Pattern Recognition Letters, vol. 27, pp. 1948-1956,2006.
[4] L. J. Guo and J. M. Moore, Pixel block intensity modulation: adding spatial detail toTM band 6 thermal imagery, Int. J. Remote Sensing., vol. 19, no. 13, pp. 2477-2491,1988.
[5] P. S. Chavez and J. A. Bowell, Comparison of the spectral information content ofLandsat thematic mapper and SPOT for three different sites in the Phoenix, Arizona
region,Photogramm. Eng. Remote Sensing., vol. 54, no.12, pp. 1699-1708, 1988.
[6] A. R. Gillespie, A. B. Kahle, and R. E. Walker, Color enhancement of highly
Correlated images-_. Channel ratio and chromaticity transformation Techniques,Remote Sensing Environment, vol. 22, pp. 343-365, 1987.
[7] J. Sun, J. Li and J. Li, Multi-source remote sensing image fusion, INT. J. RemoteSensing, vol. 2, no. 1, pp. 323-328, Feb. 1998.
[8] W. J. Carper, T. M. Lillesand, and R. W. Kiefer, The use of Intensity- Hue-
Saturation transformation for merging SPOT panchromatic and multispectral image
data,Photogramm. Eng. Remote Sensing, vol. 56, no. 4, pp. 459-467, 1990
18
8/3/2019 Image Fusion Sati
19/19
[9] K. Edwards and P. A. Davis, The use of Intensity-Hue-Saturation transformation for
producing color shaded-relief images,Photogramm. Eng. Remote Sensing, vol. 60, no.
11, pp. 1369-1374, 1994.
[10] E. M. Schetselaar, Fusion by the IHS transform: Should we use cylindrical or
Spherical coordinates?,Int. J. Remote Sensing, vol. 19, no. 4, pp. 759-765, 1998.
[11] J. Zhou, D. L. Civco, and J. A. Silander, A wavelet transform method to merge
Landsat TM and SPOT panchromatic data, Int. J. Remote Sensing, vol. 19, no. 4, pp.743-757, 1998.
[12] S. Li, J. T. Kwok, Y. Wang, Multifocus image fusion using artificial neural
networks,Pattern Recognition Letters, vol. 23, pp. 985-997, 2002.
[13] Q. Yuan, C.Y. Dong, Q. Wang, An adaptive fusion algorithm based on ANFIS for
radar/infrared system,Expert Systems with Applications, vol. 36, pp. 111-120, 2009.
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