image fusion presentation
DESCRIPTION
Image FusionPrepared by: Bhatt Mitul Introduction Developments in the field of sensing technology Multi-sensor systems in many applications such as remote sensing, medical imaging, military, etc. Result is increase of data available Can we reduce increasing volume of information simultaneously extracting all useful information?Basics of Image FusionAim of image fusion is to reduce the amount of data retain important features and create new image that is moreTRANSCRIPT
Image Fusion
Prepared by:
Rushabh P Jhaveri (15)
Introduction
Developments in the field of sensing technology Multi-sensor systems in many applications such
as remote sensing, medical imaging, military, etc. Result is increase of data available Can we reduce increasing volume of information
simultaneously extracting all useful information?
Basics of Image Fusion
Aim of image fusion is to reduce the amount of data retain important features and create new image that is more suitable for the
purposes of human/machine perception or for further processing tasks.
Single sensor image fusion system
Sequence of images are taken by a sensor Then they are fused in a image It has some limitations due to capability of sensor
Multi-sensor image fusion system
Images are taken by more than one sensor Then they are fused in a image It overcomes limitations of single sensor system
Fusion Categories
Multi-view fusion Images are taken from different viewpoints to
make 3D view
Multi-modal fusion
Multi-focus fusion
Multi-modal Fusion
N
M
R
SPECT
Fused image
Multi-focus fusion
Fused image
System level consideration
System level consideration
Three key non-fusion processes: Image registration Image pre-processing Image post processing
Continued.
Post-processing stage depends on the type of display, fusion system is being used and the personal preference of a human operator.
Pre-processing makes images best suited for fusion algorithm.
Image registration is the process of aligning images so that their details overlap accurately.
Image registration
Fields of view, resolutions, lens distortions and frame rates cannot be expected to match.
In all application fundamental problem is same; to find mapping between the pixels (x, y) in one image and the pixels (u, v) in another.
Straightforward geometric translation or rotation is the simplest technique.
Affine, polynomial and projective transformations are more advanced global approaches.
Methodology
Feature detection Algorithm should be able to detect the same features
Feature matching Correspondence between the features detected in the
sensed image and those detected in the reference image is established
Transform model estimation Type and parameters of the mapping functions are
chosen Image resampling and transformation
The sensed image is transformed
Example
How to register these two images?
The user specifies and pairs points.
Methods of Image fusion
Classification
Spatial domain fusion Weighted pixel averaging Brovey method Principal component analysis (PCA) Intensity-Hue-Saturation (IHS)
Transform domain fusion Lapacian pyramid Curvelet transform Discrete wavelet transform (DWT)
Weighted pixel averaging
Simplest image fusion technique F (x, y) = WA * A (x, y) + WB * B (x, y)
Where, WA ,WB are scalars
It has an advantage of suppressing any noise present in the source imagery.
It also suppresses salient image features, inevitably producing a low contrast fused image with a ‘washed-out’ appearance.
Pyramidal Method
Produce sharp, high-contrast images that are clearly more appealing and have greater information content than simpler ratio-based schemes.
Image pyramid is essentially a data structure consisting of a series of low-pass or band-pass copies of an image, each representing pattern information of a different scale.
Flow of pyramidal method
G1
G1 to GN
Ek
source image G 0convolution withgaussian kernal k
sub-sampling repeating until GN
duplicating each row & column of image G k+1
convolving with kLk=Gk - E krepeat until L N-1
Discrete Wavelet Transform method
It represents any arbitrary function x (t) as a superposition of a set of such wavelets or basis functions -mother wavelet by dilation or contractions (scaling) and translation (shifts)
Advantages of DWT in Image Fusion
Well suited to manage the different image resolutions. Allows the image decomposition in different kinds of
coefficients. Coefficients coming from different images can be
appropriately combined to obtain new coefficients. Final fused image is achieved through the IDWT, where
the information in the merged coefficients is also preserved.
2 level DWT of each image Low frequency sub-band is chosen based on the combined
edge information in the corresponding high frequency sub-bands.
Mean and standard deviation over 3 × 3 windows are used as activity measurement to find the edge information.
Final fused image is obtained by applying the inverse DWT on the fused wavelet coefficients.
Algorithm
Results
MMW Visible Fused
IR Visible Fused
Applications of Image Fusion
Medical image fusion
Helps physicians to extract the features from multi-modal images
Two types-structural (MRI, CT) & functional (PET, SPECT)
MRI-T2 PET Fused
Remote sensing
Remote sensing systems measure and record data about a scene.
Powerful tools for the monitoring of the Earth surface and atmosphere
Different types of images are taken by different sensors but multi-spectral and multi-polarization images are most important because they increase the separation between the segments.
So what is the requirement of image fusion in remote sensing?
Objectives of image fusion in remote sensing
Improve the spatial resolution. Improve the geometric precision. Enhanced the capabilities of features display. Improve classification accuracy. Enhance the capability of the change detection . Replace or repair the defect of image data. Enhance the visual interpretation.
Example
PAN (1 m) Color (4 m) Fused
Conclusion
Thank you