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 impr...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.展开更多
By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used bas...By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.展开更多
基金This work was supported National Key R&D Program of China(2017YFC0601505).
文摘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.
文摘By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.
