Image reconstruction via L0 gradient and L1 wavelet coefficients minimization
| Autor: | Yilin Liu, Zexian Wang, Wenbo Mei, Huiqian Du |
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| Rok vydání: | 2017 |
| Předmět: |
Optimization problem
Computer science MathematicsofComputing_NUMERICALANALYSIS ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Wavelet transform 020206 networking & telecommunications 02 engineering and technology Iterative reconstruction symbols.namesake Wavelet Fourier transform Norm (mathematics) 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Minification Algorithm |
| Zdroj: | 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). |
| Popis: | In this paper, we address the problem of image reconstruction from highly undersampled Fourier measurements. In order to promote inherent sparsity in gradient and wavelet transform domain, we proposed a new reconstruction scheme via minimizing L 0 norm of gradients and L 1 norm of wavelet coefficients. L 0 gradient minimization can control the number of non-zero gradients to enforce the sparsity in gradient, which results in edge preserving reconstruction. The reconstruction is casted into optimization framework and alternating direction method of multipliers (ADMM) algorithm is utilized to efficiently solve the proposed optimization problem. Experimental results demonstrate the superior performance of the proposed method in comparison with the L 1 gradient reconstruction method. |
| Databáze: | OpenAIRE |
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