Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform

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.

Automated summary

The research introduces a method based on fuzzy transform for adaptively identifying and reducing periodic noise peaks in image spectrum. By utilizing fuzzy transform of the spectrum to detect noise peaks and applying a notch filter for spectral smoothing, the proposed approach effectively separates the original image from noise components. Extensive experiments validate the effectiveness of the algorithm, demonstrating its superiority over existing algorithms visually and quantitatively.