7 results about "Noise generator" patented technology

The application discloses a 12-lead electrocardiosignal denoising method and system based on a dual generative adversarial network, which comprises the following steps: obtaining time-adjacent clean electrocardiosignal segments and noisy electrocardiosignal segments; constructing a noise generative adversarial network, wherein the generator takes a random noise vector as input and simulates noise as output, the discriminator takes the noisy electrocardiosignal segment as a positive sample and a synthetic signal composed of the clean electrocardiosignal segment and the simulated noise as a negative sample for adversarial training, and a noise generator snapshot library is generated; performing dynamic noise injection on the clean electrocardiosignal segment to obtain 'noisy-clean' paired data; constructing a denoising network comprising a denoiser; obtaining the 12-lead electrocardiosignal containing noise and inputting it into the denoiser to output the denoised 12-lead electrocardiosignal. The application aims to overcome the defects in the prior art, such as the lack of real 12-lead electrocardiosignal denoising paired data and the poor model generalization ability caused by excessive dependence on the Gaussian noise assumption.

This invention discloses an infrared spatiotemporal noise generation method based on hybrid neural representations. The method includes establishing an infrared denoising dataset containing paired indoor noise-clear video and unpaired outdoor noise-clear video; constructing an infrared spatiotemporal noise model based on hybrid neural representations and its training loss function; and building a spatiotemporal noise generator G and a spatiotemporal discriminator. This invention employs a divide-and-conquer approach, independently exploring the noise synthesis path from both spatial and temporal dimensions. By constructing a hybrid neural representation of noise, it deeply integrates the spatial and temporal embeddings of noise and implicitly models the complex spatiotemporal distribution of infrared noise through recurrent adversarial learning. This comprehensively and deeply characterizes the spatiotemporal properties of noise, providing new ideas and tools for noise modeling and analysis in the field of video processing.

Technology for patch-based image augmentation includes dividing an image block into image patches, each image patch having a size, shape, and location relative to the image block, generating an augmented image block by applying a variable image augmentation to each image patch, where at least two augmented image patches have different augmentation, and performing an image analysis task by applying a neural network to the augmented image block. The variable image augmentation can include at least one augmentation component selected from a plurality of augmentation components, where the augmentation components can include one or more of a filter, an intensity adjustment, a noise generator, or a style transfer. Augmentation components can be selected on a random basis or based on a predetermined queue. For an image patch having a plurality of augmentation components, outputs for the selected augmentation components are blended to provide an augmented image patch.