Periodic noise reduction is a fundamental problem in image processing, which severely affects the visual quality and subsequent application of the data. Most of the conventional approaches are only dedicated to either the frequency or spatial domain. In this research, we propose a dual-domain approach by converting the periodic noise reduction task into an image decomposition problem. We introduced a bio-inspired computational model to separate the original image from the noise pattern without having any a priori knowledge about its structure or statistics. From the filtering perspective, the proposed method filters out only a portion of the noisy frequencies. Some considerations have to be taken into account for computational resources (computing time and memory space) which permits reducing computation complexity without sacrificing the quality of the image reconstruction. In addition, the separator size provided in the decomposition algorithm does not depend on the image size. Experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. The simulation results demonstrate the effectiveness of the proposed method both qualitatively and quantitatively.

中文翻译：

---

用于盲周期噪声估计的基于自学习的图像分解：一种双域优化方法

周期性降噪是图像处理中的一个基本问题，严重影响数据的视觉质量和后续应用。大多数传统方法仅专用于频域或空间域。在这项研究中，我们提出了一种双域方法，通过将周期性降噪任务转换为图像分解问题。我们引入了一种仿生计算模型，将原始图像与噪声模式分开，而无需对其结构或统计数据有任何先验知识。从过滤的角度来看，所提出的方法仅过滤掉了一部分噪声频率。必须考虑计算资源（计算时间和存储空间）的一些考虑因素，这允许在不牺牲图像重建质量的情况下降低计算复杂度。此外，分解算法中提供的分隔符大小不依赖于图像大小。已经对合成和非合成噪声图像进行了实验，以验证所提出算法的有效性和效率。仿真结果从定性和定量两个方面证明了所提出方法的有效性。已经对合成和非合成噪声图像进行了实验，以验证所提出算法的有效性和效率。仿真结果从定性和定量两个方面证明了所提出方法的有效性。已经对合成和非合成噪声图像进行了实验，以验证所提出算法的有效性和效率。仿真结果从定性和定量两个方面证明了所提出方法的有效性。