Image reconstruction from random samples using multiscale regression framework

Jacob, Susmi; Nair, Madhu S.

doi:10.1016/j.neucom.2015.10.127

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Image reconstruction [23,24,25], which is a fundamental problem in the field 55 of image processing, aims to reconstruct the original image with high quality from the degraded measurements. Prior knowledge [26,27] plays an important role in image reconstruction and it has been proved that introducing prior knowledge can improve the efficiency of image reconstruction algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Shen

2016

Neurocomputing

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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.

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Section: Introductionmentioning

confidence: 99%