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首页> 外文期刊>Journal of Imaging Science and Technology >Adaptive Wavelet Shrinkage Based On Intelligent FIS Learned Thresholding
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Adaptive Wavelet Shrinkage Based On Intelligent FIS Learned Thresholding

机译：基于智能FIS学习阈值的自适应小波收缩

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Although wavelet shrinkage is an effective image denoising method, it tends to over-discard the image signal energy and, thus, blur the edges of the produced image. Shrinking the wavelet coefficients of all subbands indifferently is inappropriate because denoising involves not only removing high-frequency signals but also preserving image information to the greatest possible extent. To fulfill these requirements, this study presents an intelligent fuzzy inference system (FIS) learning-based thresholding strategy. First, we propose a principal directional components analysis (PDCA) method for capturing the dominant contours of an image. Along with the principal directions, the directional wavelet transform is used to provide efficient representation of the image. In addition, adaptive directional wavelet packet (WP) decomposition is used to generate the optimal WP tree. Each subband of the WP tree is denoised separately by one of the following methods: total variation denoising, soft shrinkage, and linear interpolation shrinkage. Based on the subband level and diagonality, FIS learning is used to appropriately adjust the subband threshold. Finally, individual estimates are weighted averaged to produce the denoised image. Experimental results show that compared with other denoising methods, our method not only significantly removes heavy noise, preserving more structural edge information, but also provides better peak signal-to-noise ratio and structural similarity index performances. (C) 2019 Society for Imaging Science and Technology.
机译：尽管小波收缩是一种有效的图像去噪方法，但它倾向于过度丢弃图像信号能量，从而模糊产生的图像的边缘。缩小所有子带的小波系数漠不关心地是不合适的，因为去噪不仅涉及去除高频信号，而且涉及尽可能地将图像信息保留。为了满足这些要求，本研究提出了一种基于智能模糊推理系统（FIS）基于学习的阈值策略。首先，我们提出了一种用于捕获图像的主要轮廓的主要定向分量分析（PDCA）方法。与主方向一起，定向小波变换用于提供图像的有效表示。另外，自适应定向小波分组（WP）分解用于生成最佳WP树。 WP树的每个子带是通过以下方法之一分开的分开的：总变异去噪，软缩收缩和线性插值收缩。基于子带电平和对角线，FIS学习用于适当地调整子带阈值。最后，对单独的估计是加权平均以产生去噪图像。实验结果表明，与其他去噪方法相比，我们的方法不仅显着消除了重质噪声，保留了更多的结构边缘信息，还提供了更好的峰值信噪比和结构相似性指数性能。 （c）2019年成像科技协会。

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