【摘要】：噪聲環(huán)境下圖像壓縮感知(compressive sensing,CS)重構(gòu)方法的性能會大幅度下降。在近似消息傳遞(approximate message passing,AMP)算法的基礎(chǔ)上,同時利用結(jié)構(gòu)先驗信息和邊信息來增強AMP算法對噪聲的魯棒性。利用圖像中相似塊的低秩特性,在反投影的含噪圖像中捕獲低秩子空間的結(jié)構(gòu)特征;再將含有確定成分的前期重構(gòu)圖像作為邊信息,以實現(xiàn)細節(jié)的增強。實驗表明,本文算法比原始AMP算法在峰值信噪比(peak signal to noise ratio,PSNR)上平均提高了3.89dB,且獲得更加清晰的重構(gòu)圖像;與僅利用低秩特性的AMP算法相比,引入邊信息后本文算法在PSNR上獲得了0.27dB的增益,同時增強了重構(gòu)圖像的細節(jié)。
[Abstract]:The performance of image compression sensing (compressive sensing,CS) reconstruction in noisy environment will be greatly reduced. Based on the approximate message passing (approximate message passing,AMP (approximate message passing,AMP) algorithm, the structural prior information and edge information are used to enhance the robustness of the AMP algorithm to noise. By using the low rank characteristic of similar blocks in the image, the structural features of the low rank subspace are captured in the noisy image with backprojection, and the pre-reconstructed image with deterministic components is used as edge information to enhance the details. Experimental results show that the proposed algorithm is 3.89 dB higher than the original AMP algorithm on the peak signal-to-noise ratio (peak signal to noise ratio,PSNR), and a clearer reconstructed image is obtained. Compared with the AMP algorithm which only uses the low rank characteristic, the 0.27dB gain is obtained on the PSNR by introducing edge information, and the details of the reconstructed image are enhanced at the same time.
【作者單位】： 華南理工大學(xué)電子與信息學(xué)院;
【基金】：國家自然科學(xué)基金(61471173)資助課題
【分類號】：TP391.41

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