基于混合稀疏基字典學(xué)習(xí)的微波輻射圖像重構(gòu)方法

朱路; 宋超; 劉媛媛; 黃志群; 王楊

doi:10.11999/JEIT160104

基于混合稀疏基字典學(xué)習(xí)的微波輻射圖像重構(gòu)方法

doi: 10.11999/JEIT160104

基金項目:

國家自然科學(xué)基金(31101081, 61162015)，江西省自然科學(xué)基金(20161BAB202061)

計量
- 文章訪問數(shù): 1451
- HTML全文瀏覽量: 140
- PDF下載量: 637
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-01-21
- 修回日期: 2016-08-03
- 刊出日期: 2016-11-19

Microwave Radiation Image Reconstruction Method Based on the Mixed Sparse Basis Dictionary Learning

Funds:

The National Natural Science Foundation of China (31101081, 61162015), The Natural Science Foundation of Jiangxi Province (20161BAB202061)

摘要

摘要: 目前的微波輻射測量成像系統(tǒng)在一次觀測中所采集的數(shù)據(jù)量大，基于奈奎斯特空間采樣及常規(guī)微波輻射圖像重構(gòu)方法難以實現(xiàn)高分辨率要求。該文針對微波輻射干涉測量在頻域中進行，采用傅里葉最優(yōu)隨機抽取的超稀疏干涉測量(低于奈奎斯特采樣)對微波輻射圖像進行線性壓縮投影，降低數(shù)據(jù)采樣。考慮微波輻射圖像在總體差分域和小波中都具有可壓縮特性，提出總體差分和小波混合正交基的K-SVD字典學(xué)習(xí)微波輻射圖像重構(gòu)模型，利用Bregman和交替迭代算法求解該模型，重構(gòu)線性壓縮投影信息從而獲得微波輻射圖像。仿真實驗表明，該文提出的算法在微波輻射圖像重構(gòu)效果、噪聲穩(wěn)定性上優(yōu)于DLMRI算法和GradDLRec算法。
- 微波輻射圖像 /
- 超稀疏干涉測量 /
- 混合正交基字典學(xué)習(xí) /
- 交替迭代方法
Abstract: At present, the amount of data collection of microwave radiometric imaging system in one snapshot is massive, so it is difficult to achieve the high spatial resolution by conventional microwave radiation imaging method based on the Nyquist sampling. According to the situations of microwave radiation interferometry conducted in the frequency domain, super sparse interferometry is adopted based on the optimal random Fourier sampling to sparsely project microwave radiation image, reducing the amount of data collection. Considering that the microwave radiation image has the character of compressibility in the total variation and microwave domain, the model of microwave radiation image reconstruction method is proposed based on the learning dictionary of mixed sparse basis of total variation and the wavelet, and the microwave radiation image is reconstructed by the Bregman and alternate direction method. The simulation results show that the proposed algorithm is better than the DLMRI algorithm and GradDLRec algorithm from two aspects of image reconstruction and noise sensitivity.
- Microwave Radiation Image (MRI) /
- Super sparse interferometry /
- Mixed orthogonal basis learning dictionary /
- Alternate direction method

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參考文獻(19)

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