基于图像局部滤波去噪增强算法的图像语义分割模型

Image semantic segmentation model based on local filtering noise reduction and enhancement algorithm

为解决模糊、纹理特征不均匀等复杂图像进行分割时存在的精度低问题,提出了一种基于图像局部滤波去噪增强算法的图像语义分割模型。利用Eformer去除图像中的噪声,增强图像区域的边缘;使用非参数图像定位、增强方法定位和增强目标区域,从而提高后续分割的准确性;提出基于DFANet对图像进行语义分割,从而获取目标轮廓特征。实验结果表明,与U-Net、ResU-net、R2U-Net相比,所提模型综合性能最优,分割准确率为96.60%。仿真结果验证了所提模型的有效性和实用性。

A semantic segmentation model based on image local filtering noise reduction enhancement algorithm is proposed to address the problem of low accuracy when segmenting complex images such as blurry and uneven texture features.Using the transformer to remove noise from the image and enhance the edges of the image area,it proposes non parametric image localization and enhancement methods to locate and enhance the target area,thereby improving the accuracy of subsequent segmentation.It proposes semantic segmentation of images based on DFANet to obtain target contour features.The experimental results show that compared with U-Net,ResU-net,and R2U-Net,the proposed model has the best overall performance,with a segmentation accuracy of 96.60%.The simulation results verify the effectiveness and practicality of the proposed model.