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February 17, 2021 13:51
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Implementation of basis pursuit denoising with StructuredOptimization.jl
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| using StructuredOptimization | |
| """ | |
| Simple implementation of basis pursuit denoising using StructuredOptimization.jl | |
| INPUT | |
| ----- | |
| m, n : Size of the image in both direction. | |
| idx : Linear indices of the measured pixels. | |
| y : Pixel measurements. | |
| λ : (Optional) Sparsity knob. | |
| OUTPUT | |
| ------ | |
| x : Estimated image. | |
| """ | |
| function bpdn(m, n, idx, y ; λ=0.1) | |
| # --> Initialize variable. | |
| x = Variable(eltype(y), m, n) | |
| # --> Solve the compressed sensing problem. | |
| @minimize ls(idct(x)[idx] - y) + λ*norm(x, 1) | |
| return ~x | |
| end |
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