泊松噪声在低光子计数成像中较为常见，尤其是在微光成像、天文、核医学领域，但由于建模处理信号相关噪声的困难性，对于低光子计数(及小尺寸图像)往往会存在小样本问题以及图像区块间特征自相似性不足的问题，使得当今许多优秀的降噪算法还不能达到好的降噪效果。该文提出了一种能同时解决这两种问题的泊松降噪算法。首先，我们对图像块进行分块排序，用重采样法对邻近非局部块进行分块重采样；然后选取与原始图像块近似度高的前5个采样向量并应用基于指数分布族的PCA框架对其进行处理；最后依据与原始图像近似度权重将处理结果合成降噪后的图像。实验结果显示我们的方法对小样本量低光子计数图像的泊松降噪有很好的表现。%The problem of Poisson denoising is common in various photon-limited imaging applications, especialy in low-light imaging, astronomy and nuclear medical applications. Due to the smal sample problem and the related insufficient self-similarity between patches of whole image, many denoising algorithms cannot obtain the favorable denoising performance. We propose patch-order resampling PCA algorithm for Poisson noise reduction. Firstly, we use the patch-ordered operations to sort the extracted image patches and exploit the bootstrap resampling method to resample the different blocks of spectral images to obtain more data matrix of image samples. Then, we select the patches with largest weights corresponding to the vectors of image samples data matrix as the most similar patches. Finaly, we use principal component analysis algorithm for processing the image to obtain the final denoised image. Experiments results show that the proposed method achieves excelent Poisson noise removal performance in the photon-limited images with smal sample problems.
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