2 results about "Wavelet Tree" patented technology

The invention relates to the technical field of signal processing and imaging, in particular to an inverse scattering imaging method and system based on Bayesian compressed sensing and a wavelet tree structure. Comprising the following steps: 1) acquiring original scattered field data, and preprocessing the original scattered field data; 2) constructing a wavelet tree structure based on the preprocessed data, and analyzing the wavelet tree structure to obtain prior information; 3) constructing a Bayesian compressed sensing model based on the extracted prior information and scattered field data, and solving the Bayesian compressed sensing model to obtain an estimated value of a discrete wavelet coefficient; and 4) reconstructing and optimizing the image through inverse discrete wavelet transform. According to the method, the sparsity and complexity challenges in the inverse scattering problem are effectively solved by combining the Bayesian compressed sensing and the wavelet tree structure. Rich prior information provided by a wavelet tree structure is utilized, and the accuracy and robustness of a reconstruction result are improved. Meanwhile, through an iterative optimization method, a real solution is gradually approached, and the solving efficiency is further improved.

The application discloses an image compression sensing method and device based on multi-stage information transmission enhancement. Firstly, an image is divided into blocks, then the image blocks are decomposed into low-frequency subbands and high-frequency subbands by using a discrete wavelet transform, and are pulled into vectors; next, sampling is performed according to the characteristics of different subbands, a linear inverse problem is solved by using an approximate message passing algorithm, then a reconstruction model is established by combining the prior of a wavelet tree structure and the prior of multi-stage information, and the model is expanded into a neural network; therefore, the transmission and compensation capabilities of information can be improved, the quality of current recovery is effectively improved by extracting valuable features from each stage in the past, the representation capability of the network is greatly improved, and the information loss in different stages is minimized.