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Blocking Variable Step Size Forward-Backward Pursuit Algorithm for Image Reconstruction
AiliWang,Mingji Yang,Xue Gao,Yuji Iwahori 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2
Compressed sensing is a new signal sampling theory that fully makes use of signal’s sparsity or compressibility. The theory shows that, the acquisition of a small amount of the sparse or compressible signal value can be used for exact signal reconstruction. Based on the study and summarization of the existing reconstruction algorithms, this paper proposes a novel blocking variable step size forward-backward pursuit (BVSSFBP). This paper proposed variable step size forward-backward pursuit algorithm by introducing the concept of sparse phase and variable step size to deal with different situations. The algorithm also divides two-dimensional image into blocks, in order to reduce the scale of observation matrix during single processing, reduce the single processing speed and the overall running time. Experimental results show BVSSFBP algorithm can obtain better reconstructed image quality.
Distributed Compressive Sensing based Near Infrared and Visible Images Fusion for Face Recognition
Dan Wei 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.4
In this paper, we propose a novel face recognition method based on fusing the near infrared and visible images of face images with distributed compressive sensing. The near infrared image and visible image of one same subject constitute an ensemble. Both images in one ensemble share a common sparse component while each individual image has an innovation component. To better capture the complementary information of the ensemble, the distributed compressive sensing is used to obtain the common component and the innovation component of near infrared and visible image. The obtained common component contains the complementary information of near infrared and visible image effectively. So the sparse coefficients of the common component obtained by distributed compressive sensing can better capture the intrinsic structures of each image and therefore can obtain better performance than that of only using near infrared image or visible image. The experimental results on several benchmark datasets demonstrate the effectiveness of proposed method.
Liu Bin,Yang Hongrun,Lv Huanwen,Li Lan,Gao Xilong,Zhu Jianping,Jing Futing 한국원자력학회 2020 Nuclear Engineering and Technology Vol.52 No.7
A new method of X-ray source spectrum estimation based on compressed sensing is proposed in this paper. The algorithm K-SVD is applied for sparse representation. Nonnegative constraints are added by modifying the L1 reconstruction algorithm proposed by Rosset and Zhu. The estimation method is demonstrated on simulated spectra typical of mammography and CT. X-ray spectra are simulated with the Monte Carlo code Geant4. The proposed method is successfully applied to highly ill conditioned and under determined estimation problems with a good performance of suppressing noises. Results with acceptable accuracies (MSE < 5%) can be obtained with 10% Gaussian white noises added to the simulated experimental data. The biggest difference between the proposed method and the existing methods is that multiple prior knowledge of X-ray spectra can be included in one dictionary, which is meaningful for obtaining the true X-ray spectrum from the measurements.
Accelerated Split Bregman Method for Image Compressive Sensing Recovery under Sparse Representation
( Bin Gao ),( Peng Lan ),( Xiaoming Chen ),( Li Zhang ),( Fenggang Sun ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.6
Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.
S. Nirmalraj,G. Nagarajan 한국통신학회 2021 ICT Express Vol.7 No.3
An effective visible light and infrared image fusion method using a deep learning framework is designed to obtain a fused image which contains all the features from infrared and visible images. First, the source images are decomposed into low frequency and high frequency sub bands using wavelet transform. Then the low frequency is fused by maximum fusion rule. For the high frequency sub bands a deep learning network is used to find activity level measurements and then fused using the maximum fusion rule. For reconstruction, the optimized orthogonal matching pursuit algorithm and inverse wavelet transform are used.