Based on the image self-similarity and singular value decomposition (SVD) techniques, the authors propose an iterative adaptive global denoising method. For the structural differences between image patches, they adaptively determine the size of the search window. In each window, a similar image patch matrix is constructed based on the multi-scale similarity measure. In order to ensure the speed of the method, the adaptive step size and the number of image patches are introduced, and all image patches are denoised in different iterations. This not only ensures the speed of the method, suppresses residual noise, but also reduces the artefacts caused by the fixed step size and the number of image patches. Therefore, the problem of image denoising is converted to the estimation of low-rank matrix. New singular values are estimated according to the noise level, and similar image patch matrices without noise are estimated using them and corresponding singular vectors. Experimental results show that compared with the state-of-the-art denoising algorithms, this method has a higher PSNR and FSIM, and has a good visual effect. The new method can be applied to image and video restoration, target recognition and image classification.

中文翻译：

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基于SVD的自适应迭代全局图像去噪方法

基于图像的自相似度和奇异值分解（SVD）技术，作者提出了一种迭代自适应全局去噪方法。对于图像块之间的结构差异，它们自适应地确定搜索窗口的大小。在每个窗口中，基于多尺度相似性度量构造相似的图像块矩阵。为了确保该方法的速度，引入了自适应步长和图像补丁的数量，并以不同的迭代对所有图像补丁进行了去噪。这不仅确保了该方法的速度，抑制了残留噪声，还减少了由固定步长和图像块数引起的伪影。因此，图像去噪的问题被转换为低秩矩阵的估计。根据噪声水平估计新的奇异值，并使用它们和相应的奇异矢量来估计无噪声的相似图像块矩阵。实验结果表明，与现有的去噪算法相比，该方法具有更高的PSNR和FSIM，并且具有良好的视觉效果。该新方法可以应用于图像和视频恢复，目标识别和图像分类。