最近,非局部滤波方法已成为滤波领域的研究热点.本文深入研究了基于预选择的非局部滤波方法,指出了已有方法在提取图像片特征方面存在的不足,利用二维主成分分析(Two-dimensional principal component analysis,2DPCA)提出了一种有效的...最近,非局部滤波方法已成为滤波领域的研究热点.本文深入研究了基于预选择的非局部滤波方法,指出了已有方法在提取图像片特征方面存在的不足,利用二维主成分分析(Two-dimensional principal component analysis,2DPCA)提出了一种有效的非局部滤波方法.该方法对基于预选择的非局部滤波方法的主要贡献有:1)用于提取图像片特征的面向图像片的2DPCA;2)基于相似距离直方图的相似集自动选取方法;3)相似距离权重参数局部自适应选取方法.实验结果表明,本文方法对弱梯度、人脸、纹理以及分段光滑图像均能取得较好的滤波效果.展开更多
In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enfo...In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.展开更多
文摘最近,非局部滤波方法已成为滤波领域的研究热点.本文深入研究了基于预选择的非局部滤波方法,指出了已有方法在提取图像片特征方面存在的不足,利用二维主成分分析(Two-dimensional principal component analysis,2DPCA)提出了一种有效的非局部滤波方法.该方法对基于预选择的非局部滤波方法的主要贡献有:1)用于提取图像片特征的面向图像片的2DPCA;2)基于相似距离直方图的相似集自动选取方法;3)相似距离权重参数局部自适应选取方法.实验结果表明,本文方法对弱梯度、人脸、纹理以及分段光滑图像均能取得较好的滤波效果.
基金Supported by the National Natural Science Foundation of China (No. 30900328, 61172179)the Fundamental Research Funds for the Central Universities (No.2011121051)the Natural Science Foundation of Fujian Province of China (No. 2012J05160)
文摘In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.
