Complex Texture Segmentation of Digital Painting Image Based on Widow-HoffLMS Adaptive Algorithm

Abstract

When making digital painting images, in order to ensure the visual effect of the image, it is necessary to carry out targeted processing on different areas of the image, and image segmentation is the basis to ensure the processing effect. Therefore, this study proposes a complex texture segmentation method for digital painting images based on the Widow-HoffLMS adaptive algorithm. Firstly, the Widow-HoffLMS adaptive algorithm is used to filter and process the digital painting image, and the processing ability of the Hammerstein spline optimization algorithm is introduced to avoid damaging the texture details of the image in the process of processing and ensure the detail integrity of the image; Then, the filtered digital painting image is input into the adaptive network based on ghost convolution. After the texture features in the image are extracted through the ghost convolution layer of the network, the feature map is fused and extracted through the U-Net segmentation layer; Finally, the decoder is used to restore the spatial resolution of the image and generate the final complex texture segmentation result of the digital painting image. The test results show that the filtering effect of this method is good, and the contrast of the filtered digital painting image is above 0.932; This method can complete the extraction of image texture features with different sizes and different pixel numbers, and the results of the inverse variance are all below 0.018; This method can complete complex texture segmentation of animated digital images according to the needs of texture segmentation, and the integrity of complex texture segmentation is high.