scholarly journals Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Wei Jin ◽  
Zhen Liu ◽  
Gang Li
Keyword(s):  
Compressed Sensing ◽  
High Performance ◽  
Filter Banks ◽  
Neutron Radiation ◽  
Sampling Theorem ◽  
Original Image ◽  
Compression Scheme ◽  
Directional Filter ◽  
Directional Filter Banks ◽  
Block Based

An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS), which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB) is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.

TEXTURE-BASED FINGERPRINT RECOGNITION COMBINING DIRECTIONAL FILTER BANKS AND WAVELET

2012 ◽  
Vol 26 (04) ◽  
pp. 1256012 ◽  
Author(s):  
CHAORONG LI ◽  
BO FU ◽  
JIANPING LI ◽  
XINGCHUN YANG
Keyword(s):  
Energy Method ◽  
Filter Banks ◽  
Feature Matching ◽  
Region Of Interest ◽  
Fingerprint Recognition ◽  
Edge Information ◽  
Wavelet Method ◽  
Directional Filter ◽  
Open Issue ◽  
Directional Filter Banks

To design an effective and robust fingerprint recognition method is still an open issue. Some texture-based methods such as directional energy method and wavelet method are available nowadays. However, directional energy method is insufficient to capture the detail information of fingerprint and it is also improper to directly use wavelet method to extract the feature since the complex and rich edge information of fingerprint. In this work we propose a texture-based method called DFB-Wavelet for fingerprint recognition via combining directional filter banks (DFB) and wavelet. The region of interest (ROI) composed of nonoverlapping square blocks, is decomposed into eight directions by employing DFB. Wavelet signatures are calculated as the features of a fingerprint image from each directional subband of DFB. The feature matching is performed on the global normalized Euclidean distance between the input fingerprint features and the templates features. Experimental results show that DFB-Wavelet method has the higher accuracy compared to the traditional texture-based methods.

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