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The neural network-based image semantic segmentation enhancer is a method that uses deep learning technology to semantically segment images and further enhance the segmentation results. It learns the semantic information of different objects in the image through a neural network model, segments the image into different semantic areas, and further processes and enhances each area.
Here’s a detailed explanation of how this booster works:
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Data preparation: First, a set of annotated image data sets needs to be prepared, which includes input images and corresponding semantic segmentation labels. Labels can be pixel-level annotations, with each pixel marked as belonging to a different category.
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Network architecture: Choose an appropriate neural network model as the basis for image semantic segmentation. Commonly used models include U-Net, SegNet, DeepLab, etc. These models usually consist of an encoder and a decoder to extract image features and restore resolution.
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Training process: Use the prepared data set to train the neural network model. During the training process, the input image is provided to the network for forward propagation, and the output is compared with the label to calculate the loss. Through backpropagation and optimization algorithms such as gradient descent, model parameters are updated to minimize the loss function.
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Segmentation result generation: After training is completed, new images are input into the trained model, and the segmentation results are obtained through forward propagation. The segmentation result is a matrix of the same size as the input image, where each pixel is assigned to the corresponding semantic category.