Effective Periodic Noise Reduction for Ship Corrosion Image

Abstract

Marine corrosion research has recently applied advanced image processing techniques. However, the investigation might have been interrupted due to the frequent disruption of corrosion images by certain noises, leading to a significant reduction in analysis accuracy. The comprehensive investigation of corrosion images required the implementation of progressive noise reduction techniques, especially for addressing periodic noise. Hence, the primary objective of this research is to investigate the reduction of periodic noise in ship corrosion images by leveraging a state-of-the-art image processing technique known as the Modified Gaussian Notch-Reject Filter (MGNRF). The methodology includes the selection of noise frequency using a window-based approach and the application of MGNRF for noise reduction. In comparison to other algorithms, the proposed algorithm has achieved promising results, with a mean absolute error (MAE) of 1.287, a remarkable average peak signal-to-noise ratio (PSNR) value of 40.215 and an exceptionally high mean structural similarity index (MSSIM) value of 0.998. This demonstrates the efficiency of the proposed algorithm in efficiently reducing periodic noise and enhancing the quality of ship corrosion images, thus ensuring accurate analysis and making a significant contribution to this critical domain.