15 results about "Inverse discrete wavelet transform" patented technology

The invention relates to the technical field of image information security, and discloses a controllable image steganography method and system based on a steganography diffusion model. Comprising the following steps: acquiring a hash value of a color plaintext image, and obtaining a plurality of random sequences according to the hash value and three-dimensional memristor chaotic mapping; decomposing the color plaintext image into three channel matrixes, and performing scrambling and diffusion operation on the channel matrixes based on a random sequence to generate a color ciphertext image; adding noise to the carrier image to obtain a preprocessed carrier image; obtaining a plurality of wavelet coefficient matrixes according to the preprocessed carrier image; generating a ciphertext-containing wavelet coefficient matrix according to the color ciphertext image and the wavelet coefficient matrix; performing inverse discrete wavelet transform on the confidential wavelet coefficient matrix to obtain a noisy and confidential carrier image; and de-noising the noisy and confidential carrier image by using the steganography diffusion model to generate a final confidential carrier image. According to the method, the visual quality of the carrier image can be ensured, steganography analysis is effectively resisted, and the image steganography safety is improved.

The application discloses a laser communication link correction method, system, device, medium and product based on wavelet interpolation, relates to the field of optical communication, and comprises the following steps: performing stationary wavelet transform and discrete wavelet transform on a light spot image respectively to obtain stationary subband coefficients and discrete subband coefficients; correcting the discrete high-frequency subband coefficients after interpolation processing by using the stationary high-frequency subband coefficients to obtain corrected discrete high-frequency subband coefficients; replacing the discrete low-frequency subband coefficients with the coefficients of the expanded light spot image to obtain replaced discrete low-frequency subband coefficients; through inverse discrete wavelet transform, the centroid method is used again to calculate the centroid coordinates of the light spot image after wavelet interpolation processing and the deflection angle of the beacon light, the deflection angle of the receiving end is adjusted according to the deflection angle of the beacon light, and the correction of the laser communication link is completed; and the stability of the laser communication link can be improved by improving the subdivision accuracy of the light spot.

The application belongs to the field of three-dimensional reconstruction, and particularly relates to a micro-morphology analysis method based on wavelet multi-layer frequency band decomposition. The method comprises the following steps: firstly, a multi-focus image sequence of a micro scene to be measured is collected, and multi-scale spatial features are obtained through feature coding and projection mapping; then, a two-dimensional discrete wavelet transform method is introduced, the spatial features are decoupled into a low-frequency approximate subband representing global geometry and a high-frequency detail subband representing fine texture, and structure feature correction and directional texture enhancement are respectively implemented; on this basis, inverse discrete wavelet transform and residual fusion are used to construct time-frequency enhanced features, a deep regression network is used to analyze pixel-level focus probability distribution to generate a continuous depth map; finally, a cascading mechanism driven by three-dimensional geometric reconstruction for scene morphology perception is established, and depth geometric edge constraints are used to reversely guide the high-fidelity generation of a full-focus image. Through the wavelet multi-layer frequency band decomposition strategy, the application effectively solves the problem of confusion between texture details and out-of-focus noise in micro imaging, and realizes the synchronous robust analysis of high-precision geometric structure and full-depth clear texture of micro morphology.

The application discloses a beacon light centroid positioning method and device based on improved wavelet threshold in optical communication, and relates to the technical field of optical communication. The method comprises the following steps: acquiring multiple beacon light images; performing median filtering processing and discrete wavelet transform on each image, acquiring multiple wavelet decomposition multi-layer coefficient matrices, and obtaining a denoising matrix by using a VisuShrink method; performing inverse discrete wavelet transform on the denoising matrix to obtain multiple denoising light spot images; calculating the centroid coordinates of light spots in each denoising light spot image by using a gray centroid method; and performing summation and average processing on the centroid coordinates of the multiple images to obtain beacon light centroid positioning coordinates. By improving the wavelet threshold processing, the application effectively removes noise interference, improves the accuracy and stability of beacon light centroid positioning, and provides reliable technical support for accurate positioning in optical communication.

The invention relates to the technical field of sequence recommendation and AI intelligence, in particular to a sequence recommendation method based on a wavelet hybrid expert framework. The method comprises the following steps: acquiring a historical interaction sequence of a user, and converting the historical interaction sequence into embedded representation; performing discrete wavelet transform on the embedded representation, and decomposing the embedded representation into a low-frequency approximation coefficient and a high-frequency detail coefficient; directly reserving the low-frequency approximation coefficient as a trend expert for representing the long-term preference of the user; for the high-frequency detail coefficient, generating a time refining feature through a learnable weight matrix multiplied by the high-frequency detail coefficient element by element; local experts representing instantaneous interests of the user are obtained through a context-aware router; inverse discrete wavelet transform is carried out, and enhanced embedding representation is reconstructed; and adopting a multi-task learning strategy, and carrying out joint training to generate a recommendation model. According to the method, the accuracy and robustness of the recommendation result are improved.

The invention discloses a meta-optimization adversarial sample generation algorithm combined with frequency domain feature fusion. The meta-optimization adversarial sample generation algorithm comprises the following steps: S1, frequency domain feature decomposition; s2, low-frequency reconstruction: reconstructing a low-frequency reconstructed image of the input image by adopting inverse discrete wavelet transform, and calculating a corresponding high-frequency image according to an original image and a low-frequency reconstructed difference value; s3, feature storage: respectively inputting the low-frequency image and the high-frequency image of the clean sample into a white box model, and extracting and storing features corresponding to each layer; s4, performing feature fusion; s5, inputting the fusion features obtained in the step S4 into a subsequent layer of the white box model to obtain model output; and S6, executing a meta-optimization strategy, calculating and combining internal and external gradient information to update the adversarial sample, and outputting a finally generated adversarial sample. According to the method, the image is decomposed and reconstructed in the frequency domain, and the multi-frequency features and the meta-optimization updating strategy are extracted and fused respectively, so that conflicts among different frequency domain features are effectively relieved, and the attack mobility of the generated confrontation sample to a conventional model and a defense model is improved.

The invention provides a dosing control method, system and equipment for a water treatment process and a medium, and the method comprises the steps: carrying out the discrete wavelet multi-scale decomposition of a standardized input sequence, and obtaining a low-frequency trend component and a high-frequency detail component; inputting the standardized input sequence into a large language sub-model of a pre-trained prediction model to obtain a high-dimensional time sequence feature; inputting the low-frequency trend component, the high-frequency detail component and the high-dimensional time sequence feature into the prediction model to generate a prediction coefficient of the fusion feature; and performing discrete wavelet inverse transformation recombination processing on the prediction coefficient of the fusion feature to obtain an expected output sequence of the water quality parameter data at the future moment, extracting the instantaneous dosing flow in the expected output sequence, and performing dosing control on the water treatment process according to the extracted instantaneous dosing flow. According to the invention, the expected accurate control of the dosing treatment at the future moment can be improved.

An image processing apparatus and an image processing method thereof are disclosed. The image processing apparatus includes a discrete wavelet transform (DWT) device that performs DWT and down-sampling on a first source image to split the first source image into a low-frequency sub-image including low-frequency components in horizontal and vertical directions and a plurality of high-frequency sub-images including high-frequency components in at least one of the horizontal or vertical directions; a frame memory that stores a low-frequency sub-image of a second source image; a first noise reduction device that reduces noise in the low-frequency sub-image of the first source image using the low-frequency sub-image of the second source image; and an inverse discrete wavelet transform (IDWT) device that applies IDWT to the low-frequency sub-image of the first sub-image and the high-frequency sub-images of the first sub-image, which are noise-reduced by the first noise reduction device, to restore the first source image.

The invention discloses a dual-channel-based digital watermark processing method and device and electronic equipment, and relates to the technical field of digital watermarking. A dual-channel-based digital watermark processing method comprises a watermark embedding process: performing discrete wavelet transform decomposition on a first audio signal to obtain a low-frequency approximate component and a high-frequency detail component; respectively performing singular value decomposition on the low-frequency approximate component and the high-frequency detail component to obtain a high-frequency singular value and a low-frequency singular value; constructing a second channel according to a ratio feature formed by mapping the high-frequency singular value and the low-frequency singular value, and embedding watermark information; performing inversion calculation on a low-frequency singular value of the embedded watermark based on each ratio characteristic component in the second channel of the embedded watermark information, and reconstructing the low-frequency singular value into a low-frequency approximate component of the embedded watermark; and carrying out inverse discrete wavelet transform processing on the watermark-embedded low-frequency approximate component and the watermark-embedded high-frequency detail component to obtain a watermark-embedded second audio signal.

The invention belongs to the technical field of image encryption, and particularly provides a frequency domain image encryption method and system based on SVD and double random phase coding, a hash function is applied to a plaintext image P to generate a hash value K, two chaotic sequences X and Y are further generated based on the hash value K, and six chaotic sequences X, Y, W, Z, J and L are finally obtained after preprocessing; performing wavelet transform operation on the plaintext image P to obtain a sub-image, and performing hybrid operation on the sub-image and the six chaotic sequences to obtain a reconstructed sub-image; and performing diffusion processing on the reconstructed sub-image to obtain a new sub-image, and performing inverse discrete wavelet transform to obtain a final encrypted image E. According to the method, the singular value matrix is dynamically recombined, and the key sensitivity of the chaotic system is introduced, so that the ciphertext histograms tend to be uniformly distributed, the attack based on the pixel statistical law is effectively resisted, meanwhile, the frequency domain structure correlation is destroyed, and the encryption robustness is improved.

The invention discloses a medical image registration method based on wavelet guidance and cross-scale frequency domain correction, and the method comprises the steps: obtaining a moving image and a fixed image in a single-channel gray 3D volume data form, defining a dense displacement field (DVF), learning a mapping relation through a network, and carrying out the standardization preprocessing of the images; and respectively inputting the moving image and the fixed image into two weight sharing paths Pm and Pf of a wavelet pyramid encoder in the medical image registration model, decomposing and extracting multi-scale wavelet features layer by layer through discrete wavelet transform, and respectively outputting a moving image feature pyramid and a fixed image feature pyramid. The decoder part guides Swin Transform to reconstruct a deformation field layer by layer through inverse discrete wavelet transform, and meanwhile, a cross-scale frequency domain self-correction module is introduced to optimize the deformation field layer by layer and suppress error accumulation. Compared with a spatial domain registration method in the prior art, the method breaks through the limitation of spatial domain registration, realizes space-frequency domain collaborative optimization through full-stage wavelet guidance, and remarkably improves the registration precision.

The present application relates to the technical field of smart grid, and proposes a method and system for suppressing subsynchronous oscillation of a mining area power supply system, comprising: collecting different types of electric data of each power shovel in the mining area power supply system, and obtaining an electric data sequence of each power shovel; marking a target power shovel, performing multi-layer wavelet decomposition on the electric data sequence of the target power shovel, obtaining all detail coefficients of each layer, and determining an adaptive adjustment threshold of the detail coefficients of the same layer; determining a consistency eigenvalue of each type of electric data of the target power shovel, obtaining an adjustment value of the detail coefficients of each layer of the same type of electric data of the power shovel, and obtaining a denoised electric data sequence of the power shovel according to the detail coefficients and inverse discrete wavelet transform; and separating subsynchronous oscillation fault data according to the denoised electric data sequence, so as to realize suppression of subsynchronous oscillation of the mining area power supply system. The present application can improve the effect of suppressing subsynchronous oscillation of the power supply system.

The invention relates to the technical field of data enhancement, and provides a wavelet and Fourier dual-domain data enhancement method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a labeled sample image and an unlabeled sample image; performing discrete wavelet transform processing on the labeled sample image to obtain a first low-frequency sub-band and a first high-frequency sub-band of the labeled sample image, and performing discrete wavelet transform processing on the unlabeled sample image to obtain a second low-frequency sub-band and a second high-frequency sub-band of the unlabeled sample image; replacing the low-frequency feature of the first low-frequency sub-band with the low-frequency feature of the second low-frequency sub-band in the frequency domain through Fourier transform to obtain a third low-frequency sub-band after feature replacement; and performing discrete wavelet inverse transform processing on the third low-frequency sub-band and the first high-frequency sub-band to obtain an enhanced sample image. By adopting the method, sample images with image style differences can be generated to increase the diversity of training data, so that the generalization ability of an image segmentation model is improved.

This invention discloses an anti-interference perceptual encryption method for UAV images, belonging to the field of image encryption technology. The method constructs a joint deep learning model, including a visual encryption module, a visual decryption module, and a differential attack module based on a bidirectional reversible network. During training, the UAV image is processed through discrete wavelet transform, the bidirectional reversible network, and inverse discrete wavelet transform to generate an encrypted image and key information. The differential attack module simulates interference to generate an interfered encrypted image; this image, along with the key, is then input into the decryption module to recover the original image. The encryption and decryption modules are simultaneously optimized using a composite loss function. This method achieves perceptual encryption and high-fidelity reversible recovery, possesses excellent anti-interference capabilities, is suitable for UAV transmission scenarios, supports real-time processing and high-security decryption, and can be deployed on airborne edge computing platforms or cloud systems, meeting privacy protection requirements.

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.