【摘要】：全連接的玻爾茲曼機模型可全面描述稀疏系數(shù)間統(tǒng)計依賴關(guān)系,但時間復(fù)雜度較高.為了提高基于玻爾茲曼機的貝葉斯匹配追蹤算法(BM-BMP)的重構(gòu)速度和質(zhì)量,本文提出一種改進(jìn)算法.第一,將BM-BMP算法的最大后驗概率(MAP)估計評估值分解為上一次迭代的評估值與增量,使得每次迭代僅需計算增量,極大縮短了計算耗時.第二,利用顯著最大后驗概率估計值平均的方式,有效近似最小均方誤差(MMSE)估計,獲得了更小的重構(gòu)誤差.實驗結(jié)果表明,本文算法比BM-BMP算法的運行時間平均縮短了73.66%,峰值信噪比(PSNR)值平均提高了0.57 d B.
[Abstract]:The fully connected Boltzmann machine model can fully describe the statistical dependence between sparse coefficients, but the time complexity is high. In order to improve the reconstruction speed and quality of Bayesian matching tracking algorithm (BM-BMP) based on Boltzmann machine, an improved algorithm is proposed in this paper. Firstly, the maximum posterior probability (MAP) estimation of BM-BMP algorithm is decomposed into the evaluation value and increment of the previous iteration, which makes each iteration only need to calculate the increment, which greatly shortens the computation time. Secondly, the minimum mean square error (MMSE) estimation is effectively approximated by the average value of the significant maximum posterior probability, and a smaller reconstruction error is obtained. The experimental results show that the average running time of this algorithm is 73.66 shorter than that of BM-BMP algorithm, and the (PSNR) value of peak signal-to-noise ratio is increased by 0.57 dB on average.
【作者單位】： 華南理工大學(xué)電子與信息學(xué)院;國家移動超聲探測工程技術(shù)研究中心;
【基金】：國家自然科學(xué)基金資助項目(61327005,61302120) 廣東省科技計劃資助項目(2017A020214011) 中央高�；究蒲袠I(yè)務(wù)費資助項目(2017MS039)
【分類號】：TN911.7

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