Filtering multi-dimensional images such as color images, color videos,
multispectral images and magnetic resonance images is challenging in terms of
both effectiveness and efficiency. Leveraging the nonlocal self-similarity
(NLSS) characteristic of images and sparse representation in the transform
domain, the block-matching and 3D filtering (BM3D) based methods show powerful
denoising performance. Recently, numerous new approaches with different
regularization terms, transforms and advanced deep neural network (DNN)
architectures are proposed to improve denoising quality. In this paper, we
extensively compare over 60 methods on both synthetic and real-world datasets.
We also introduce a new color image and video dataset for benchmarking, and our
evaluations are performed from four different perspectives including
quantitative metrics, visual effects, human ratings and computational cost.
Comprehensive experiments demonstrate: (i) the effectiveness and efficiency of
the BM3D family for various denoising tasks, (ii) a simple matrix-based
algorithm could produce similar results compared with its tensor counterparts,
and (iii) several DNN models trained with synthetic Gaussian noise show
state-of-the-art performance on real-world color image and video datasets.
Despite the progress in recent years, we discuss shortcomings and possible
extensions of existing techniques. Datasets and codes for evaluation are made
publicly available at this https URL

本研究比较了超过 60 种图像去噪方法，其中 BM3D 方法在各种去噪任务中展现出了有效性和高效性，同时发现多种 DNN 模型能够在对抗真实数据的去噪任务中展现出最优的性能。该研究还引入了一个新的彩色图像和视频数据集用于基准测试。