Adaptive image segmentation technology of wavelet domain based on generative adversarial networks
When the image contrast is low or the lighting conditions are complex,the image target shape,color and other characteristics have high variability,and it is difficult to accurately identify the target boundary,resulting in a low degree of overlap between the target area and the real annotation in the image segmentation results.To this end,the wavelet domain adaptive image segmentation technology based on generative adversarial networks is studied.Using the wavelet domain analysis basis function,the image is converted from the spatial domain to the fuzzy set domain.Considering the image contrast,the diffusion intensity of the image transformation is calculated.By using anti blur transformation function to restore the blurring effect in the image,the image with enhanced target edges is obtained,and the similarity between image pixels is calculated.The relative entropy and weight of the segmented image are calculated by introducing the distance information,furthermore to optimize the processing tasks of the generative adversarial network and realize the adaptive segmentation of the image.The experimental results show that the proposed technology can accurately segment the image and retain the rich detailed information.The average overlap rate reaches 96.3%,and the segmentation accuracy is high,indicating that the technology has high reliability in the image segmentation task and provides high quality input datas for the subsequent image processing tasks.
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