标题： 使用全局和局部循环表示的高效图像去噪 显示英文标题

标题： Efficient Image Denoising Using Global and Local Circulant Representation

摘要： 成像设备的进步和每天生成的大量图像数据对高效且有效的图像去噪提出了越来越高的要求。 在本文中，我们提出了一种计算简单的去噪算法，称为Haar-tSVD，旨在探索非局部自相似性先验，并利用主成分分析（PCA）与循环表示下的Haar变换之间的联系。 我们证明，通过带有Haar变换的统一张量奇异值分解（t-SVD）投影，可以有效地捕捉全局和局部块相关性。 这导致了一种一步式、高度可并行化的滤波方法，消除了对学习局部基来表示图像块的需求，在去噪速度和性能之间取得了平衡。 此外，我们引入了一种基于CNN估计器和特征值分析的自适应噪声估计方案，以增强所提出方法的鲁棒性和适应性。 在不同的现实世界去噪任务上的实验验证了Haar-tSVD在噪声去除和细节保留方面的效率和有效性。 数据集、代码和结果可在 https://github.com/ZhaomingKong/Haar-tSVD 公开获取。 显示英文摘要

摘要： The advancement of imaging devices and countless image data generated everyday impose an increasingly high demand on efficient and effective image denoising. In this paper, we present a computationally simple denoising algorithm, termed Haar-tSVD, aiming to explore the nonlocal self-similarity prior and leverage the connection between principal component analysis (PCA) and the Haar transform under circulant representation. We show that global and local patch correlations can be effectively captured through a unified tensor-singular value decomposition (t-SVD) projection with the Haar transform. This results in a one-step, highly parallelizable filtering method that eliminates the need for learning local bases to represent image patches, striking a balance between denoising speed and performance. Furthermore, we introduce an adaptive noise estimation scheme based on a CNN estimator and eigenvalue analysis to enhance the robustness and adaptability of the proposed method. Experiments on different real-world denoising tasks validate the efficiency and effectiveness of Haar-tSVD for noise removal and detail preservation. Datasets, code and results are publicly available at https://github.com/ZhaomingKong/Haar-tSVD.