A fast parallel primal-dual based compressed sensing method for 2-D near-field SAR image reconstruction

Lyu, Jue; Bi, Dongjie; Xie, Yongle; Xie, Xuan

doi:10.1109/i2mtc.2018.8409813

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 b . Besides, all parameters for reconstructing the LR image had the same set as in [5 ]. After finishing the reconstruction of the LR image, the cubic spline interpolation method was employed to enlarge the resolution of the image.…”

Section: Resultsmentioning

confidence: 99%

“…We chose USR

ξ = 20 normal%

for the random under‐sampling process. For comparison, we utilised the MMW image reconstruction algorithm [5 ] with the same USR

ξ

to achieve an LR image, which is shown in Fig. 2 b .…”

Section: Resultsmentioning

confidence: 99%

“…Test SUTs and results of experiment (

ξ = 20 normal%

ρ = 4

) a Photography of test SUTs b Reconstructed LR image with algorithm [5 ] c Interpolated SR image from reconstructed LR image d Reconstructed SR image with proposed algorithm…”

Section: Resultsmentioning

confidence: 99%

“…For a monostatic 2D near‐field MMW imaging system, the measurements are sampled by a single transceiver antenna that scans over a 2D gridded plane. The compressive 2D MMW system reconstructs an image by very few measurements while under‐sampling from random spatial points with compressed sensing (CS) techniques [4, 5 ]. Although CS techniques extremely enhance the sensing process, resolution of the reconstructed image still is restricted by the wavelength, which directly decided the maximum of scan grid interval [1, 6 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Super‐resolution image reconstruction of compressive 2D near‐field millimetre‐wave

Lyu

Xie

et al. 2020

Electron. lett.

Self Cite

View full text Add to dashboard Cite

Compressive 2D near‐field millimetre‐wave (MMW) imaging is a compressed sensing‐based technique, which reconstructs an image from a few under‐sampled measurements. Although this technique can extremely improve imaging process efficiency, resolution of the reconstructed image is still limited by the scanning grid interval, which is always hard to be improved in practice due to hardware constraint. To reconstruct a higher resolution image from under‐sampled measurements, a compressive 2D near‐field MMW super‐resolution (SR) imaging model is established. Meanwhile, the corresponding algorithm, which is based on the primal‐dual framework, is also proposed. Experimental results show that the proposed algorithm can effectively reconstruct the SR MMW image in superior performance.

show abstract

Section: Resultsmentioning

confidence: 99%

“…We chose USR

ξ = 20 normal%

for the random under‐sampling process. For comparison, we utilised the MMW image reconstruction algorithm [5 ] with the same USR

ξ

to achieve an LR image, which is shown in Fig. 2 b .…”

Section: Resultsmentioning

confidence: 99%

“…Test SUTs and results of experiment (

ξ = 20 normal%

ρ = 4

) a Photography of test SUTs b Reconstructed LR image with algorithm [5 ] c Interpolated SR image from reconstructed LR image d Reconstructed SR image with proposed algorithm…”

Section: Resultsmentioning

confidence: 99%