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Introduction
Image fusion – a technique that integrates complementary
information from multiple image sensor data such that the new image are more suitable for processing tasks.
The fusion of images is often required for images acquired from different instrument modalities or capture techniques of the same scene or objects.
Image fusion is the process by which two or more images are combined into a single image retaining the important features from each of the original images.
FUSION METHODS
Linear superposition Nonlinear methods Optimization approaches Artificial neural networks Image pyramids Wavelet transform Generic multiresolution fusion scheme
DECISION RULE BASED IMAGE FUSION USING WAVELET
TRANSFORM
In recent years, many solutions to image fusion have been proposed. This paper presents an effective multi-resolution image fusion methodology, which is wavelet based image fusion. Fusion process is applied in the clinical case: the study of some particular
disease by MR/SPECT fusion. The effectiveness of the proposed model is demonstrated via results comparison with several other image fusion methods.
Literature survey
The technique that was used before was called multi resolution analysis
Existing System
Existing System
Fusion framework in feature-level. Effective multi-sensor image data fusion methodology on
the basis of discrete wavelet transform theory Self-Organizing Neural Network.
Proposed System
Fusion framework in Decision level
Using discrete wavelet transform method
Fuzzy logic Neural Networks
Wavelet Transform
What is wavelet Transform: Wavelet Transform is a type of signal
representation that can give the frequency content of the signal at a particular instant of time.
Wavelet Transform
Why need wavelet transform? Wavelet analysis has advantages over
traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.
1D Discrete Wavelet Transform
2D Discrete Wavelet Transform
New Approach
Discrete wavelet transform can offer a more precise way for image analysis.It decomposes a image into low frequency band and high frequency band in different levels, and it can also be reconstructed at these levels.
When images are merged in this method different frequencies are processed differently.
Improves the quality of the new image since it works on Feature extraction.
The fusion algorithm is performed at the pixel level.
DWT Sub-band Structure
L
H
2L
H
L
H
Horizontal(Rows) Vertical(Columns)
N/2 x MN/2 x M/2
LL
LH
HL
HH 2
2
2
2
2
Image with resolution Level R
N x M
L: Lowpass filter
H: Highpass filter
2: downsample by 2
Detail Image corresponding to information visible at the resolution Level R
Image corresponding to resolution Level R-1
DWT Sub-band Structure
LL: Horizontal Low pass& Vertical Low pass
LH: Horizontal Low pass& Vertical High pass
HL: Horizontal High pass& Vertical Low pass
HH: Horizontal High pass& Vertical High pass
DWT Sub-band Structure
Stage 1
Stage 2
Stage 3
DWT with D=3 stages
A DWT Example
LL1
HH1HL1
LH1
HH2
LH2HH2
LH
2
HL
2
HL2
LL2
LL0
Functional Flow Diagram
Input Image A
Wavelet decomposition
Filtering in the domain of spatial frequency
Fusion Rules
Fusion Decision MapInverse wavelet decomposition
Fused Image
Input Image B
Image reconstruction
Functional Flow Diagram 2
Implementation
Relevant wavelet theory
Since image is 2-D signal, we will mainly focus on the 2-D wavelet transforms.
After one level of decomposition, there will be four frequency bands, namely Low-Low (LL), Low-High (LH), High-Low (HL) and High-High (HH).
Implementation
The next level decomposition is just apply to the LL band of the current decomposition stage, which forms a recursive decomposition procedure.
The frequency bands in higher decomposition levels will
have smaller size.
GUI - EXISTING TECHNIQUES
GUI – WAVELET APPROACH
GUI – FUZZY BASED
GUI – WAVELET AND FUZZY BASED
Advantages
No need to divide the input coding into non-overlapping 2-D blocks, it has higher compression ratios avoid blocking artifacts.Allows good localization both in time and spatial
frequency domain.Transformation of the whole image introduces
inherent scalingBetter identification of which data is relevant to
human perception higher compression ratio (64:1 vs. 500:1)
Applications
NAVIGATION AID
MEDICAL IMAGING
REMOTE SENSING
MERGING OUT-OF-FOCUS IMAGES
Applications
Intelligent robots
•Require motion control, based on feedback from the environment from visual, tactile, force/torque, and other types of sensors •Stereo camera fusion •Intelligent viewing control •Automatic target recognition and tracking
Applications
Medical image•Fusing X-ray computed topography (CT) and magnetic resonance (MR) images
• Computer assisted surgery
• Spatial registration of 3-D surface
Applications
Manufacturing
• Electronic circuit and component inspection • Product surface measurement and inspection
non-destructive material inspection • Manufacture process monitoring • Complex machine/device diagnostics • Intelligent robots on assembly lines
Applications
Military and law enforcement
• Detection, tracking, identification of ocean (air,ground)target/event
• Concealed weapon detection
• Battle-field monitoring
• Night pilot guidance
References
BASE PAPER : DAVID L. HALL and JAMES LLINAS, An Introduction to Multisensor Data Fusion, Proceedings of IEEE, 85, 1 (Jan.
1997) Barbara Zitova, Jan Flusser, Image registration methods: a survey. Image and Vision Computing 21
References
RELATED PAPERS : L. J. Chipman and T. M. Orr, “Wavelets and image fusion,” in Proceedings of the IEEE International Conference on Image Processing, Washington D.C., October 1995, pp. 248– 251 (2003) L.J. Chipman, T.M. Orr, and L.N. Lewis. Wavelets and image
fusion.IEEE Transactions on Image Processing, 3:248–251, 1995. linage fusion techniqcs Sinione.Giovanni and Farina. Alfonso and
Morahito. Francesco and Scmico. Sebastiano Bruno and Bruzzone.Lorcnzo (U). Technical Report DIT-02-025, University of Trento.