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长春光学精密机械与物... [2]
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会议论文 [2]
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2011 [1]
2010 [1]
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Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE)
会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.
;
Wang M.-J.
;
Han G.-L.
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提交时间:2013/03/25
Being an efficient method of information fusion
image fusion has been used in many fields such as machine vision
medical diagnosis
military applications and remote sensing.In this paper
Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing
including segmentation
target recognition et al.
and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First
the two original images are decomposed by wavelet transform. Then
based on the PCNN
a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength
so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So
the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment
the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range
which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore
by this algorithm
the threshold adjusting constant is estimated by appointed iteration number. Furthermore
In order to sufficient reflect order of the firing time
the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved
each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules
the experiments upon Multi-focus image are done. Moreover
comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
An improved fast parallel SPIHT algorithm and its FPGA implementation (EI CONFERENCE)
会议论文
2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, May 21, 2010 - May 24, 2010, Wuhan, China
Wu Y.-H.
;
Jin L.-X.
;
Tao H.-J.
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提交时间:2013/03/25
In view of the current stringent need to the real-time compression algorithm of the high-speed and high-resolution image
such as remote sensing or medical image and so on
in this paper
No List SPIHT (NLS) algorithm has been improved
and a fast parallel SPIHT algorithm is proposed
which is suitable to implement with FPGA. It can deal with all bit-planes simultaneously
and process in the speed of 4pixels/period
so the encoding time is only relative to the image resolution. The experimental results show that
the processing capacity can achieve 200MPixels/s
when the input clock is 50MHz
the system of this paper need 2.29ms to complete lossless compression of a 512x512x8bit image
and only requires 1.31ms in the optimal state. The improved algorithm keeps the high SNR unchanged
increases the speed greatly and reduces the size of the needed storage space. It can implement lossless or lossy compression
and the compression ratio can be controlled. It could be widely used in the field of the high-speed and high-resolution image compression. 2010 IEEE.
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