The advancement of imaging devices and countless images generated everyday pose an increasingly high demand on image denoising, which still remains a challenging task in terms of both effectiveness and efficiency. To improve denoising quality, numerous denoising techniques and approaches have been proposed in the past decades, including different transforms, regularization terms, algebraic representations and especially advanced deep neural network (DNN) architectures. Despite their sophistication, many methods may fail to achieve desirable results for simultaneous noise removal and fine detail preservation. In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We also introduce a new dataset for benchmarking, and the evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Our experiments demonstrate: (i) the effectiveness and efficiency of representative traditional denoisers for various denoising tasks, (ii) a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts, and (iii) the notable achievements of DNN models, which exhibit impressive generalization ability and show state-of-the-art performance on various datasets. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets, code and results are made publicly available and will be continuously updated at https://github.com/ZhaomingKong/Denoising-Comparison.

翻译：成像设备的进步和每天生成的无数图像对图像去噪提出了日益增长的需求，在效果和效率方面这仍然是一项具有挑战性的任务。为了提高去噪质量，过去几十年中提出了大量去噪技术和方法，包括不同的变换、正则化项、代数表示，特别是先进的深度神经网络（DNN）架构。尽管这些方法非常复杂，但许多方法可能无法在同时去除噪声和保留精细细节方面达到理想效果。在本文中，为了探究现有去噪技术的适用性，我们针对不同应用，在合成和真实数据集上比较了多种去噪方法。我们还引入了一个用于基准测试的新数据集，并从四个不同角度进行评估，包括定量指标、视觉效果、人类评分和计算成本。我们的实验表明：（i）代表性传统去噪器在各种去噪任务中的有效性和效率，（ii）一个简单的基于矩阵的算法可能产生与其张量对应方法相似的结果，以及（iii）DNN模型的显著成就，这些模型展现出令人印象深刻的泛化能力，并在各种数据集上达到了最先进的性能。尽管近年来取得了进展，我们讨论了现有技术的不足和可能的扩展。数据集、代码和结果已在 https://github.com/ZhaomingKong/Denoising-Comparison 公开提供，并将持续更新。