Research on metal surface specular removal algorithm based on unsupervised learning

基于无监督学习的金属表面高光去除算法研究

¹ School of Software, Northwestern Polytechnical University, Xi'an 710072, China
² Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
³ Shanghai Aerospace Precision Machinery Research Institute, Shanghai 201699, China

Received: 20 July 2023

Abstract

The highlights on the surface of metal materials can seriously destroy the continuity of the image, produce certain false edges, and cause the texture details in the highlights area to weaken or even disappear, which interferes with the subsequent operations such as surface region segmentation and defect detection. Aiming at the low efficiency, high loss, easy distortion and difficult calibration of metal highlight data, an unsupervised perceptual enhancement network model based on the convolutional neural network (CNN) is proposed. Firstly, the method of generating antagonism is used to generate a large number of metal images with high-light feature information, which is used to increase the number of high-light metal image data sets in the training set. Secondly, a detail enhance model(DEM) and a color enhance model(CEM) are introduced into the context aggregation network to improve the feature detail retention rate in the low resolution weight graphs. Finally, the multi-scale structural similarity function is used to replace the original structural similarity function to solve the insensitive detail when the image size is too large. Experiments show that comparing with other multi-exposure image fusion models, the present model can improve the evaluation index of mutual information and average gradient of fused images by about 10%, and can retain more texture feature information.

摘要

金属材料表面的高光会严重破坏图像的连续性, 产生一定的伪边缘, 导致高光区域的纹理细节减弱甚至消失, 干扰后续表面的区域分割和缺陷检测等操作。针对现有高光去除方法效率低、损失大、易失真、金属高光数据难标定等问题, 提出了一种基于卷积神经网络(CNN)的无监督感知增强网络模型。使用生成对抗的方法生成大量拥有高光特征信息的金属图像, 用于增加训练集中高光金属图像数据集的数量; 在上下文聚合网络中引入细节增强模块(detail enhance model, DEM)和色彩增强模块(color enhance model, CEM)提高低分辨率权重图中的特征细节保留率; 评估函数采用多尺度结构相似性函数代替原有结构相似性函数, 解决在图像尺寸过大时细节不敏感的问题。实验表明, 所提模型相比其他多曝光图像融合模型, 在融合速度领先情况下, 融合图像的互信息和平均梯度等评估指数提升10%左右, 能够保留更多的纹理特征信息。