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：随着成像设备的进步以及每日产生的海量图像，对图像去噪的需求日益增长，然而在有效性和效率方面，这仍是一项富有挑战性的任务。为提升去噪质量，过去几十年间研究者提出了大量去噪技术和方法，涵盖不同变换、正则化项、代数表示，特别是先进的深度神经网络（DNN）架构。尽管这些方法复杂精密，但许多方法仍难以在去除噪声与保留精细细节之间取得理想平衡。为探究现有去噪技术的适用性，本文针对不同应用场景，在合成数据集和真实世界数据集上比较了多种去噪方法。我们还引入了一个新的基准测试数据集，并从定量指标、视觉效果、人工评分和计算成本四个维度进行评估。实验结果表明：(i) 代表性传统去噪器在各种去噪任务中具有有效性和高效性；(ii) 简单的基于矩阵的算法可能产生与其张量版本相似的结果；(iii) DNN模型取得了显著成就，展现出令人印象深刻的泛化能力，并在多个数据集上达到最优性能。尽管近年来取得了进展，我们仍讨论了现有技术的不足及可能的拓展方向。数据集、代码及结果已公开，并将在https://github.com/ZhaomingKong/Denoising-Comparison持续更新。