4 results about "Wavelet coding" patented technology

The application provides an embryo development stage classification method and system based on residual perception and wavelet coding, and belongs to the embryo development stage classification field.The method comprises the following steps: acquiring an embryo image and performing pretreatment; performing a blocking operation on the pretreated embryo image, inputting the blocked image data into a residual perception attention module, and extracting a first feature map of the embryo image; inputting the first feature map into a wavelet position coding module, and extracting a second feature map containing frequency domain information; adding the first feature map and the second feature map and inputting the sum into a trained deep neural network, performing global feature extraction and coding, and obtaining a final feature map; inputting the final feature map into a classifier for development stage classification, and outputting a classification result of the embryo development stage. The method effectively overcomes the problem of spatial information loss in the downsampling method, enhances the sensitivity of the model to the frequency domain information, and improves the accuracy of the embryo development stage classification.

The application discloses a kind of space-time prediction methods based on wavelet coding and space-frequency dual-domain feature fusion, belong to space-time prediction technical field, comprising: obtaining by multiple time steps consisting of source space-time sequence, constructs input sequence and target sequence;Through wavelet downsampling, the input sequence is encoded step by step, and the spatial semantic representation with multi-scale characteristics is extracted;The representation after coding is organized as feature sequence according to time sequence, and time series modeling is carried out;Space-time feature containing state information and change clues is input into space-time conversion network, to realize cross space-time feature interaction;Through inverse wavelet reconstruction, the features after space-time conversion are decoded step by step, and future prediction sequence is generated.The application solves the problem that key information is easy to lose due to the use of conventional convolution sampling in existing methods, solves the problem that existing methods mainly rely on single spatial domain transformation, resulting in limited feature modeling, solves the problem that existing methods mainly rely on implicit learning, resulting in indirect time series modeling.

The application discloses an image enhancement network method based on distribution alignment prior and wavelet coding and decoding, comprising the following steps: S1, super-lens infrared image data preparation and degradation analysis; S2, constructing a VAR distribution alignment domain adaptation module; S3, constructing an instruction-guided diffusion model module; S4, constructing a wavelet-guided codec module; S5, constructing a cross-modal feature fusion module: integrating complementary information from different representation spaces, and deeply cooperating the VAR distribution alignment domain adaptation module, the instruction-guided diffusion model module and the wavelet-guided codec module to jointly constitute an infrared super-lens image enhancement network; and S6, end-to-end image enhancement and reasoning deployment. Through multi-modal feature interaction and frequency domain adaptive fusion, the application realizes physical consistent high-quality image reconstruction.

The invention discloses a medical image segmentation method based on wavelet condition potential diffusion, and relates to the technical field of medical image segmentation, and the method comprises the following steps: obtaining a medical input image, executing a structure-maintained color enhancement operation in a training stage, and using an original input image in a reasoning stage; the preprocessed image is embedded by adopting a multi-scale wavelet encoder extraction condition, and the encoder is realized through multi-layer discrete wavelet transform, high-frequency residual enhancement, inverse discrete wavelet transform and down-sampling splicing; encoding a segmentation target corresponding to the input image through a pre-trained and parameter-frozen auto-encoder to obtain a potential mask; inputting the semantic priori condition and the potential mask into a diffusion UNet, embedding a frequency domain sensing gating block in jump connection of the diffusion UNet, and performing potential space denoising through a regularization iterative algorithm to obtain an optimized potential mask; and decoding the optimized potential mask through a decoding function of the auto-encoder, and outputting a final medical image segmentation result.