多尺度多阶段特征融合的带噪图像语义分割

在图像的采集过程中,图像往往会带有一定的噪声信息,这些噪声信息会破坏图像的纹理结构,进而干扰语义分割任务.现有基于带噪图像的语义分割方法,大都是采取先去噪再分割的模型.然而,这种方式会导致在去噪任务中丢失语义信息,从而影响分割任务.为了解决该问题,提出了一种多尺度多阶段特征融合的带噪图像语义分割的方法,利用主干网络中各阶段的高级语义信息以及低级图像信息来强化目标轮廓语义信息.通过构建阶段性协同的分割去噪块,迭代协同分割和去噪任务,进而捕获更准确的语义特征.在PASCAL VOC 2012和Cityscapes数据集上进行了定量评估,实验结果表明,在不同方差的噪声干扰下,模型依旧取得了较好的分割结果.

Semantic Segmentation of Noisy Images with Multi-scale and Multi-stage Feature Fusion

In the process of image acquisition, the image often contains certain noise information, which will destroy the texture structure of the image and thus interfere with semantic segmentation tasks. Most of the existing semantic segmentation methods based on noisy images adopt models featuring first denoising and then segmentation. However, they often lead to the loss of semantic information in denoising tasks, which thus affects segmentation tasks. To solve this problem, this study proposes a multi-scale and multi-stage feature fusion method for semantic segmentation of noisy images, which uses the high-level semantic information and low-level image information of each stage in the backbone network to enhance the semantic information of target contours. By constructing a staged collaborative segmentation denoising block, collaborative segmentation and denoising tasks are iterated, and then more accurate semantic features are captured. In addition, quantitative evaluation is carried out on PASCAL VOC 2012 and Cityscapes datasets. The experimental results show that the model still achieves positive segmentation results under the noise interference of different variances.