GA-BFO based signal reconstruction for compressive sensing

2016

Signal Processing

Self Cite

l 0 minimization based sparse reconstruction is an NP-hard problem with very high computational complexity, which is difficult to be achieved by traditional algorithms. Although greedy algorithm aims at solving l 0 minimization, it is more likely to obtain a sub-optimal solution. In this paper, we propose an intelligent greedy pursuit (IGP) algorithm to solve the l 0 minimization essentially. Firstly, we propose a novel optimization function for the sparse reconstruction problem with the sparsity level unknown as a prior. Then, a two-cycle optimization algorithm is designed, whose object is to estimate the support collection and its corresponding coefficients intelligently and accurately by searching for the global optimal solution. To this end, we take advantage of intelligent optimization algorithm in global searching and solving combinatorial optimization problems to guide the intelligent estimation. Also, the principle of estimation is designed by the matching strategies of greedy algorithm which performs quite well in reconstruction speed. The so-called IGP model is as simple as greedy algorithm, while it has been proved through experiments that the performance of IGP for signal reconstruction and image reconstruction outperforms the state-of-the-art reconstruction algorithms. Moreover, IGP can reconstruct signal accurately with a relatively small measurement rate.

Section: Connections Between Igp and Existing Workmentioning

confidence: 99%

Section: Connections Between Igp and Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent greedy pursuit model for sparse reconstruction based on l0 minimization

2016

Signal Processing

Self Cite

“…Also, the precondition of [17,18] is that the sparsity level must be known, which is a very strong limitation as the sparsity level is always unknown in practice. In our precious 50 work [19,20], we use genetic algorithm and artificial immune algorithm [21,22] to solve l 0 minimization directly. However, due to the randomness of intelligent optimization algorithm, the computational complexity of the proposed methods in [19,20] is high and the reconstruction speed of them slow down.…”

Section: Introductionmentioning

confidence: 99%

Multi-variable intelligent matching pursuit algorithm using prior knowledge for image reconstruction by l0 minimization

2016

Neurocomputing

Self Cite

Image reconstruction by l 0 minimization is an NP-hard problem that requires exhaustively listing all possibilities of the original signal with a very high computational complexity, which is difficulty to be achieved by traditional algorithms.Although greedy algorithm aims at solving l 0 minimization, it is more likely to fall into a suboptimal solution. In this paper, we propose a multi-variable intelligent matching pursuit algorithm (MIMP), which can solve l 0 minimization problem essentially by taking the advantage of the intelligent optimization algorithm in solving combinatorial optimization problems and searching for the global optimal solution to improve the performance of image reconstruction.The updating mechanism of MIMP is designed by introducing the matching strategies of greedy algorithm to accelerate the reconstruction speed. Also, the multi-variable scheme is utilized to sample images and then the joint reconstruction is implemented to the measurements, which can not only improve the reconstruction accuracy but also reduce the computational complexity. Moreover, the edge saliency can be obtained as the prior knowledge to guide the compressive sensing reconstruction, which contributes a lot to reduce the computational complexity and accelerate the reconstruction speed. As the sparsity level of image is hard to be estimated, a new optimization function is proposed to solve this problem without knowing the sparsity level as a prior. Compared with other state-of-the-art algorithms, the proposed method MIMP can achieve a better reconstruction accuracy by solving l 0 minimization essentially with intelligent optimization algorithms. Also, MIMP has a reasonable relatively faster reconstruction speed by introducing the matching strategies of greedy algorithm and using the edge saliency as a prior knowledge. Numerical experiments on several images demonstrate that the proposed method MIMP significantly outperforms the state-of-the-art algorithms and the structure based reconstruction algorithms in PSNR, SSIM and visual quality.

“…However, the sparsity level is hard to be estimated in many reconstruction problems. In our previous work [21,22], we propose two reconstruction methods based on the combination of genetic algorithm and bacterial foraging optimization (GA-BFO) algorithm and artificial immune algorithm (AIA), which perform quite well in reconstruction accuracy. Nevertheless, the computational complexity of the two methods are high, which leads to the slow reconstruction speed.…”

Section: Introductionmentioning

confidence: 99%

Predicted multi-variable intelligent matching pursuit algorithm for image sequences reconstruction based on l0 minimization

Journal of Visual Communication and Image Representation

2016

Self Cite

In this paper, we study the problem of reconstructing image sequences which satisfy the conditions that (a) the sparsity level is high in the wavelet domain and (b) the sparsity pattern of adjacent images changes very slowly. The idea of the proposed method predicted multi-variable intelligent matching pursuit (PMIMP) algorithm is to use the estimated support collection of the previous image as prior information and then utilize the prior information to guide the current image reconstruction by solving l 0 minimization. Multi-variable scheme is used to sample image sequences to enhance the guidance of prior information and improve the reconstruction accuracy with fewer measurements.