Scratch detection and removal method for coal microscopic images
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摘要: 煤岩显微图像预处理主要包括煤岩划痕检测与去除。针对基于霍夫变换算法的煤岩划痕检测难以准确提取空间形状特征和有效细化边缘信息，容易出现漏检和误检的问题，提出了基于语义分割的煤岩划痕检测方法。该方法引入残差结构改进空间注意力模型，将该模型嵌入以VGG卷积层作为图像特征编码器的U-Net中，实现对煤岩划痕的语义分割。针对基于快速行进的图像修复算法使得煤岩划痕去除区域和周围区域存在纹理差异和视觉伪影的问题，提出了采用基于改进区域匹配的图像修复算法去除煤岩划痕。通过采用k个最近邻图像块查找、跨尺度及旋转角度搜索策略和基于欧氏距离的图像块偏移距离度量，实现煤岩划痕的有效去除。实验结果表明，基于语义分割的煤岩划痕检测方法能准确反映煤岩划痕的边缘细节，具有较好的空间特征解析性能，提高了煤岩划痕检测准确性；采用基于改进区域匹配的图像修复算法去除煤岩划痕能使煤岩划痕去除区域与周围区域的纹理特征更具有一致性，提升图像整体视觉效果。Abstract: Coal microscopic image preprocessing mainly includes coal scratch detection and removal. It is difficult to extract spatial shape characteristics accurately and refine edge information effectively for coal scratch detection based on the Hough transform algorithm and it is prone to miss detection and false detection. In order to solve the above problems, a coal scratch detection method based on semantic segmentation is proposed. This method introduces the residual structure to improve the spatial attention model, and embeds the model into U-Net which uses the VGG convolutional layer as the image characteristic encoder to obtain the semantic segmentation of coal scratches. In order to solve the problem that the fast-moving image restoration algorithm makes the texture difference and visual artifacts between the coal scratch removal area and the surrounding area, an image restoration algorithm based on improved area matching is proposed to remove coal scratches. The effective removal of coal scratches is achieved by using k-nearest neighbor image block search, cross-scale and rotation angle search strategies, and an image block offset distance measurement based on Euclidean distance. The experimental results show that the coal scratch detection method based on semantic segmentation can reflect the edge details of coal scratches accurately, has better spatial characteristic analysis performance, and improves the accuracy of coal scratch detection. The method adopts the image restoration algorithm based on improved area matching to remove coal scratches. Therefore, the texture characteristics of the coal scratch removal area and the surrounding area are more consistent, and the overall visual effect of the image is improved.
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