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首页> 外文期刊>Journal of the Optical Society of America, A. Optics, image science, and vision >Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain
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Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain

机译：小波域中使用块压缩感知和平滑投影Landweber积分的自适应多焦点图像融合

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The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches. (C) 2016 Optical Society of America
机译：当前图像处理系统中对图像融合的需求在增加，这主要是由于图像采集技术的数量和种类的增加。图像融合是使用数学技术将来自多个传感器的大量信息组合在一起以创建单个合成图像的过程，该合成图像将更加全面，因此对于操作员或其他计算机视觉任务更加有用。本文提出了一种基于稀疏信号表示的多焦点图像融合新方法。与鼓励小波域稀疏性的投影驱动压缩感测（CS）恢复集成的基于块的压缩感测用作从一组散焦图像中获取聚焦图像的方法。使用块压缩感测方法在图像采集过程中实现压缩。平滑投影的Landweber恢复过程中的自适应阈值技术可根据离焦图像的低维CS测量重建高分辨率聚焦图像。离散小波变换和双树复数小波变换被用作融合的稀疏基础。主要发现在于，稀疏化可以更好地选择融合系数，从而实现更好的融合。在小波域中完成了Laplacian混合模型拟合，并且通过期望最大化对概率密度函数（pdf）参数的估计使我们能够正确选择融合图像的系数。与不采用投影Landweber（PL）方案的融合方案和其他现有的基于CS的融合方法相比，将本方法与融合方案相比，可以观察到，所用方法本身样本较少，优于其他方法。 （C）2016美国眼镜学会

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