## Chang Wang and Wen Zhang## |

Method | /PSNR | SSIM | ||||
---|---|---|---|---|---|---|

20/22.1182 | 25/20.1800 | 30/18.5964 | =20 | =25 | =30 | |

TV | 26.9777 | 25.9039 | 25.0694 | 0.7575 | 0.7137 | 0.6765 |

K-SVD | 27.2776 | 26.1625 | 25.3488 | 0.7574 | 0.7080 | 0.6681 |

BM3D | 27.0646 | 25.9662 | 25.1284 | 0.7533 | 0.7065 | 0.6678 |

Proposed method | 27.3271 | 26.2920 | 25.4504 | 0.7771 | 0.7345 | 0.6973 |

It can be seen that the TV method's PNSR values are statistically lower than those of the K-SVD and BM3D methods, but the SSIM values are higher than those of these two methods, as shown in Fig. 5(a), 5(b) and Table 1. It indicates that the TV method can retain more detailed image edge information, but the denoising effect is poor. In contrast, the K-SVD method and BM3D method have better denoising effects but at the cost of losing more detailed information about the image edge (as shown in Fig. 4). The suggested method's PSNR and SSIM values are greater than those of the TV, BM3D, and K-SVD methods, as shown in Fig. 5(a), 5(b) and Table 1. The suggested strategy can reduce picture random noise while retaining more precise image edge information.

In order to ensure that the proposed method is stable, another image (the size of the image is 256 × 256 pixel) is selected for denoising, and the experimental results and image denoising evaluation indexes are shown in Figs. 6, 7 and Table 2.

The model constructed in this paper can effectively remove noise based on the results of Figs. 6, 7 and Table 2. Fig. 8 compared with the TV, K-SVD, and BM3D methods. Furthermore, more detailed information on the image edge can be preserved.

This study proposes a wavelet-based IGK-SVD-VDM denoising method that preserves more image edge details. The simulation experiments demonstrate that the new method effectively denoises random noise while retaining more detailed information on the image edge. Furthermore, the denoising image makes a better visual impression.

Although the proposed method has a good denoising effect, it has some drawbacks. It is easy to lose details when using the constructed variational denoising method to remove high-frequency components, especially those decomposed in the diagonal direction.

This research was supported by the Fund project of the Provincial Education Department (No. LJKMZ20220638) and the Open Fund Project of the Marine Information Technology Innovation Center of the Ministry of Natural Resources. We appreciate the time and thought given by the anonymous reviewers who diligently reviewed this letter and gave valuable feedback.

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