In this study, a new adaptive morphological filter is developed based on the mathematical morphology algorithm and characteristics of the subtle differences in the waveform morphology in seismic data. The algorithm improves the traditional morphological dilation and corrosion operations. In this study, we propose a multiscale adaptive operator based on the principle of morphological structural “probes” and present the corresponding mathematical proof. Simulation experiments and actual seismic data processing results show that compared with traditional morphological filters, the constructed OCCO-based multistructure adaptive morphological filter can suppress noise to the greatest extent. Moreover, it can effectively improve the SNR of the images, and offers great application prospects.

Study and application analysis of random noise adaptive morphological filter algorithm reconstruction for seismic signals

Image segmentation plays an important role in wear particles analysis. A new segmentation method based on multiscale mathematical morphology is proposed for wear particles image segmentation. The newly introduced method employs different scale structuring elements to detect the image edge, the final edge is calculated by the weighted average method. Edge details can be remained by small scale structuring element (SE) and noise can be depressed effectively by large scale SE, therefore, the new method has great effect in edge accuracy, strong and weak edge extraction and noise suppression. The efficiency of the method is evaluated by a set of wear particles images. The comparison with the single scale SE and other traditional methods demonstrates the improvement of the new algorithm.

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Compared with wavelet transform, Curvelet transform has characteristics of anisotropy and good curve singularity expression abilities. To advance the validity and reliability of wear particle feature extraction as well as recognition, an image feature enhancement method based on Curvelet transform was proposed. Wear particle images were decomposed into different frequency components by Curvelet transform. Scale enhancement coefficients were introduced into the medium-frequency and high-frequency components to enhance images’ edges and details. Then, enhanced wear particle images were achieved utilizing inverse Curvelet transform. Experiment results indicate that the proposed method can effectively improve image quality. As the details and edges are clear, enhanced images are more suitable for feature extraction and recognition.

Guo De Wang

Papers

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Feature Enhancement of Wear Particle Images Based on Curvelet Transform