贝叶斯精化近似网络的表示与分析
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey
摘要：
在人工模型背景减除之后，像素被标记为前景和背景。以前的方法，二次处理的输出去噪通常使用传统的方法，如贝叶斯细化方法。在本文中，我们专注于使用一个修改的U-网模型来近似贝叶斯细化方法的结果，并改善结果。在我们改进的U-Net模型中，其他模型的背景减除结果将与源图像相结合，作为学习统计分布的输入。这样，就可以从源图像中恢复出由背景减除模型造成的信息丢失。而且，由于输入图像的一部分已经是另一个背景减除模型的输出，所以特征提取应该是方便的，它只需要改变噪声像素的标签。与传统方法相比，使用深度学习方法在保持细节方面具有优势。
英文摘要：
After an artificial model background subtraction, the pixels have been labelled as foreground and background. Previous approaches to secondary processing the output for denoising usually use traditional methods such as the Bayesian refinement method. In this paper, we focus on using a modified U-Net model to approximate the result of the Bayesian refinement method and improve the result. In our modified U-Net model, the result of background subtraction from other models will be combined with the source image as input for learning the statistical distribution. Thus, the losing information caused by the background subtraction model can be restored from the source image. Moreover, since the part of the input image is already the output of the other background subtraction model, the feature extraction should be convenient, it only needs to change the labels of the noise pixels. Compare with traditional methods, using deep learning methods superiority in keeping details.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
