## Ming Ming*## |

Template size | Parameter setting |
---|---|

[TeX:] $$8 \times 8$$ | 0.456 |

[TeX:] $$12 \times 12$$ | 0.424 |

[TeX:] $$30 \times 30$$ | 0.554 |

[TeX:] $$36 \times 36$$ | 0.535 |

[TeX:] $$40 \times 40$$ | 0.578 |

The main experimental indicators selected in the comparative experiment are as follows:

(1) Signal-to-noise ratio (SNR) of output results: SNR refers to the ratio of effective information to noise in the output results. The higher the SNR value is, the more effective information content is. The calculation process is as follows:

where, [TeX:] $$P_s$$ and [TeX:] $$P_n$$ represent the power values of the effective signal and noise, respectively.

(2) Segmentation time: the evaluation process time can reflect the efficiency of the image segmentation algorithm, and the consumption time is automatically counted by the simulation platform.

(3) Segmentation accuracy: The validity of different segmentation algorithms can be detected according to the segmentation accuracy.

Firstly, the SNR of the output results of different algorithms is tested.

With the increase of the number of experimental iterations, the SNR of the output evaluation results of different image segmentation algorithms change constantly. However, the SNR peak value of the output results of the proposed algorithm remains the highest, with the maximum value reaching 73.67 dB. Then the segmentation process time of different algorithms is tested.

With the increase of the number of experimental iterations, the time consumption of the segmentation process of different image segmentation algorithms changes constantly in an irregular law. However, the segmentation time of the proposed algorithm remainsless, of which the maximum time is only 7 seconds.

Secondly, the validity of different algorithms is verified by taking the segmentation accuracy as an indicator, with the increase of the number of experimental iterations, the segmentation accuracy changes constantly. The segmentation accuracy of the proposed algorithm decreases first and then increases, with the global maximum value reaching 97%, which is significantly higher than that of the other two algorithms. Therefore, the algorithm proposed in this paper can effectively achieve accurate segmentation of VDBIs.

To improve the image segmentation quality and efficiency, this paper proposes an accurate VDBI segmentation algorithm based on improved wavelet transform.

By smooth processing of VDBI, the traditional wavelet transform process is improved, and the two-layer decomposition of dynamic image is realized by using two-dimensional wavelet transform. On the basis of decomposition results and information enhancement processing, image features are detected, feature points are extracted, and quantum ant colony algorithm is adopted to complete accurate segmen¬tation of the image.

In order to verify the feasibility of the proposed segmentation algorithm, simulation experiments are carried out. The images in the form of character scene images, landscape images and remote sensing images are selected. The proposed method is adopted to segment these three types of images. The results indicate the feasibility of the proposed algorithm. To further highlight the forward-looking technical level of the algorithm proposed in this paper, the comparative experiments are carried out among the proposed algorithm and the other two literature methods in terms of SNR of output result, segmentation time and segmentation accuracy. The experimental results show that the peak value of the SNR of the output results of the proposed algorithm is always the highest, with the maximum value reaching 73.67 dB; the segmentation time of the proposed algorithm is always less, and the maximum segmentation time is only 7 seconds; the segmentation accuracy of the proposed algorithm shows a trend of decreasing first and then increasing, with the global maximum reaching 97%, which is significantly higher than that of the other two algorithms. Therefore, it shows that the image segmentation algorithm proposed in this paper has good application performance in improving the SNR of output results, shortening the time-consuming process, improving the segmentation accuracy.

Although the proposed algorithm improves the wavelet transform, the inherent limitations of wavelet transform, such as information redundancy, still limit its application. In addition, there is a lack of systematic and standardized optimal wavelet basis selection methods. These problems need to be solved in future researches.

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