Periodic noise degrades the image quality by overlaying similar patterns. This noise appears as peaks in the image spectrum. In this research, a method based on fuzzy transform has been developed to identify and reduce the peaks adaptively. We convert the periodic noise removal task as image compression and a smoothing problem. We first utilize the direct and inverse fuzzy transform of the spectrum to detect periodic noise peaks. Second, we propose a fuzzy transform-based notch filter for spectral smoothing and separating the original image from the periodic noise components. This noise correction approach filters out a portion (given by fuzzy transform) of the noise component. Extensive 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 that the proposed method outperforms state of the art algorithms both visually and quantitatively.

周期性噪声会通过覆盖相似的图案而降低图像质量。该噪声显示为图像光谱中的峰值。在这项研究中，开发了一种基于模糊变换的方法来自适应地识别和减少峰值。我们将周期性噪声去除任务转换为图像压缩和平滑问题。我们首先利用频谱的直接和逆模糊变换来检测周期性的噪声峰值。其次，我们提出了一种基于模糊变换的陷波滤波器，用于频谱平滑和将原始图像与周期性噪声分量分离。这种噪声校正方法会滤除噪声分量的一部分（通过模糊变换给出）。已经对合成和非合成噪声图像进行了广泛的实验，以验证所提出算法的有效性和效率。