A New Image Fusion Algorithm Based on Curvelet Transform
Abstract
In recent times, the applications of image processing have grown immensely. Usually due to
limited depth of field of optical lenses especially with greater focal length, it becomes impossible
to obtain an image where all the objects are in focus. Image fusion deals with creating an image
in which all the objects are in focus. Thus it plays an important role to perform other tasks of
image processing such as image segmentation, edge detection, stereo matching and image
enhancement.
This project analyzes the characteristics of the Second Generation Curvelet Transform and put
forward an image fusion algorithm based on Wavelet Transform and the Curvelet Transform. We
looked at the selection principles about low and high frequency coefficients according to
different frequency domain after Wavelet and the Second Generation Curvelet Transform. In
choosing the low-frequency coefficients, the concept of local area variance was chosen to
measuring criteria. In choosing the high frequency coefficients, the window property and local
characteristics of pixels were analyzed.
Finally, the proposed algorithm in this article was applied to experiments of multi focus image
fusion and complementary image fusion. According to simulation results, the proposed algorithm
holds useful information from source multiple images quite well
(1).Introduction
Image fusion is a data fusion technology which keeps images as main research contents.
It refers to the techniques that integrate multi-images of the same scene from multiple image
sensor data or integrate multi-images of the same scene at different times from one image sensor.
The image fusion algorithm based on Wavelet Transform which faster developed was a multi
resolution analysis image fusion method in recent decade. Wavelet Transform has good timefrequency characteristics. It was applied successfully in image processing field. Nevertheless, its
excellent characteristic in one-dimension can’t be extended to two dimension or multi-dimension
simply. Separable wavelet which was spanning by one-dimensional wavelet has limited
directivity. Aiming at these limitation, E. J. Candes and D. L. Donoho put forward Curvelet
Transform theory in 2000 . Curvelet Transform consisted of special filtering process and multiscale Ridgelet Transform. It could fit image properties well.
2. OBJECTIVE:
However, Curvelet Transform had complicated digital realization, includes sub-band
division, smoothing block, normalization, Ridgelet analysis and so on. Curvelet’s pyramid
decomposition brought immense data redundancy. Then E. J. Candes put forward Fast Curvelet
Transform(FCT) that was the Second Generation Curvelet Transform which was more simple
and easily understanding in 2005. Its fast algorithm was easily understood. Li Huihui’s
researched multi-focus image fusion based on the Second Generation Curvelet Transform
3. Proposed scheme:
This project analyzes the characteristics of the Second Generation Curvelet Transform and put
forward an image fusion algorithm based on Wavelet Transform and the Curvelet Transform. We
looked at the selection principles about low and high frequency coefficients according to
different frequency domain after Wavelet and the Second Generation Curvelet Transform. In
choosing the low-frequency coefficients, the concept of local area variance was chosen to
measuring criteria. In choosing the high frequency coefficients, the window property and local
characteristics of pixels were analyzed. Finally, the proposed algorithm in this article was
applied to experiments of multi focus image fusion and complementary image fusion. According
to simulation results, the proposed algorithm holds useful information from source multiple
images quite well
4. Software and hardware requirements

Operating system
:
Windows XP/7.

Coding Language
:
MATLAB

Tool
:
MATLAB R 2012
System requirements:
Hardware requirements:

System
:
Pentium IV 2.4 GHz.

Hard Disk
:
40 GB.

Floppy Drive
:
1.44 Mb.

Monitor
:
15 VGA Colour.

Mouse
:
Logitech.

Ram
:
512 Mb.
5. Conclusion:
This paper puts forward an image fusion algorithm based on Wavelet Transform and the Second
Generation Curvelet Transform. It includes multiresolution analysis ability in Wavelet
Transform, also has better direction identification ability for the edge feature of awaiting
describing images in the Second Generation Curvelet Transform. This method could better
describe the edge direction of images, and analyzes feature of images better. According to it, this
paper uses Wavelet and the Second Generation Curvelet Transform into fusion images, then
makes deep research on fusion standards and puts forward corresponding fusion projects. At last,
these fusion methods are used in simulation experiments of multi-focus and complementary
fusion images. In vision, the fusion algorithm proposed in this paper acquires better fusion result.
In objective evaluation criteria, its fusion characteristic is superior to traditional DWT and
FCT’s.
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