51 results about "Inverse discrete wavelet transform" patented technology

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.

The invention relates to a face depth forgery active defense method and device based on discrete wavelet transform, and relates to the technical field of image processing, and the method comprises the steps: carrying out the attribute editing of an original image and a disturbance-containing image through a depth forgery model, calculating the mean square error loss and structural similarity loss between the original image and the disturbance-containing image, different weights are endowed and fused into a total loss function; gradient information is obtained based on loss function back propagation, the optimal direction of disturbance application is determined, and an intermediate disturbance value is formed; low-frequency and high-frequency components are embedded into corresponding components of the disturbance-containing image through discrete wavelet transform, and then the image is reconstructed through inverse discrete wavelet transform; checking whether the number of iterations is reached or not, and if not, introducing a JPEG noise layer to continuously iterate and adjust disturbance; and if yes, outputting a final face image containing invisible disturbance information. According to the method, the robustness in the wavelet domain is fully utilized, and the resistance of disturbance to JPEG compression is improved on the premise of ensuring that the disturbance is invisible.

The invention discloses a semi-supervised underwater image enhancement system and method based on wavelet transform and a diffusion model. The semi-supervised underwater image enhancement system comprises an image decomposition module used for obtaining a low-frequency component and a high-frequency component of an image to be enhanced by using discrete wavelet transform; the low-frequency diffusion module is used for gradually and sequentially denoising the randomly generated pure noise image by using a trained neural network model in a semi-supervised underwater enhanced image model through taking the low-frequency component as condition guidance to obtain an enhanced low-frequency component; the high-frequency repairing module is used for obtaining an optimized high-frequency component; and the inverse discrete wavelet transform module is used for fusing the optimized high-frequency component and the enhanced low-frequency component through inverse discrete wavelet transform to obtain a reconstructed enhanced image. According to the method, the wavelet transform is fused into the diffusion model, and the image enhancement process is optimized by utilizing the semi-supervised learning model and cooperatively utilizing the annotated data and the annotated data, so that the problem that the data scale is limited is solved, and the calculation efficiency in the image enhancement process is improved.

The invention belongs to the technical field of image encryption, and discloses a medical image compression and encryption method and system based on a memristor FHN neuron model, and the method comprises the steps: fusing n color or gray medical images of any size into an M * N * 3 fusion image; sparse representation is carried out on the fused medical image in combination with discrete wavelet transform, and redundant data are reduced; initial conditions and control parameters of an m-FHN chaotic system are generated through an SHA-512 hash value of an original image, and strong association of a secret key and a plaintext is ensured; the m-FHN system is iterated to generate three groups of chaos sequences, and the chaos sequences are quantized to obtain a pseudo-random matrix for diffusion and scrambling operation; performing forward diffusion, index scrambling and backward diffusion operation on the sparse image in sequence to obtain a final compressed and encrypted image; a receiving end acquires an initial parameter and a control parameter bound with a plaintext through a secure channel, and then performs inverse backward diffusion, inverse index scrambling, inverse forward diffusion and inverse discrete wavelet transform on a ciphertext image in sequence to obtain a decrypted image.

The invention provides an SM2-based certificateless identity authentication method and system, and the method comprises the steps: obtaining a to-be-authenticated identity data package, decrypting the identity data package based on an SM2 algorithm, and obtaining a main singular value sequence and a user public key; performing XOR decryption processing on the main singular value sequence and a private key corresponding to the user public key to obtain an original main singular value sequence; performing reconstruction processing on the original main singular value sequence to obtain a reconstructed singular value matrix; performing inverse discrete wavelet transform based on the reconstructed singular value matrix to obtain reconstructed watermark data; and matching the reconstructed watermark data with original watermark data in a database, and obtaining an identity authentication result according to a matching result. According to the method, the decryption mechanism of the SM2 algorithm is introduced, the identity authentication mechanism and the characteristics of the watermark data are deeply fused, and the defect that identity authentication based on the watermark data cannot be efficiently and accurately achieved at present is overcome.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an audio watermark embedding method and device, computer equipment and a storage medium, and the method comprises the steps: decomposing a host audio to be embedded with a watermark into a plurality of sub-bands with different frequency bands through discrete wavelet transform; performing layer-by-layer down-sampling on each sub-band by using an encoder in a U-Net network model, and extracting hidden space features of the host audio; determining watermark information to be embedded, and encoding the watermark information to obtain a watermark vector; embedding the watermark vector into the hidden space feature to obtain a fusion feature; carrying out layer-by-layer up-sampling on the fusion features by using a decoder in the U-Net network model, and reconstructing audio information; performing inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information; therefore, the flexibility, adaptability and robustness of audio watermark embedding can be effectively improved.

The invention discloses an image fusion method and device based on discrete wavelet transform and a convolutional neural network, a terminal and a storage medium. The method comprises the steps of preprocessing a received original image group to obtain an input image group; performing multi-scale decomposition on the input image group through DWT to obtain a decomposition result, and performing feature extraction on the decomposition result according to CNN to obtain an infrared low-frequency feature, an infrared high-frequency feature, a visible low-frequency feature and a visible high-frequency feature respectively; performing fusion through a preset feature fusion strategy to obtain a low-frequency fusion feature and a high-frequency fusion feature; and performing feature reconstruction on the low-frequency fusion features and the high-frequency fusion features through a preset inverse discrete wavelet transform algorithm to obtain and output a fusion image of the original image group. According to the method provided by the invention, the multi-scale characteristic of the DWT is combined with the depth feature extraction capability of the CNN, so that the comprehensive and accurate fusion of the image information is realized, and the quality and efficiency of image fusion are greatly improved.

The invention relates to an infrared and visible light image frequency domain diffusion fusion method, and belongs to the technical field of image processing. Comprising the following steps: inputting an infrared image and a visible light image into a potential space encoder to form original bimodal features; a Gaussian noise is randomly sampled, the Gaussian noise and the original bimodal features are input into a wavelet denoising network together to be decomposed into a low-frequency component and three high-frequency components, the bimodal high-frequency components are fused through FMM, and the bimodal low-frequency components are fused through FMM at the lowest scale; carrying out inverse discrete wavelet transform on the fused low-frequency component and high-frequency component to obtain estimated noise, and then carrying out noise removal on the estimated noise to obtain de-noising features; carrying out iterative optimization and feature updating to obtain fusion features; and inputting the fusion features into a potential space decoder to obtain a fusion image. The fusion quality of the infrared and visible light images can be comprehensively improved.

The invention discloses a super-resolution structured light reconstruction method based on a wavelet enhanced CNN-Transformer structure. The super-resolution structured light reconstruction method comprises the following steps of: firstly, carrying out wavelet enhancement on a CNN-Transformer structure; according to the method, image reconstruction is realized based on a WECT network adopting a coding-decoding architecture; the method comprises the following steps: firstly, extracting initial features through shallow convolution, performing two-stage down-sampling by using discrete wavelet transform, and respectively inputting results into a convolutional neural network and a Swin Transform module to perform parallel feature extraction and fusion; secondly, performing two-stage up-sampling through inverse discrete wavelet transform, and performing jump connection with shallow layer features; finally, pixel shuffling up-sampling operation is introduced, and residual jump connection between the input image and the output image is established; according to the method, high-resolution details can be efficiently recovered while the structure reduction precision is ensured, the image definition and contrast are enhanced, and the application value and practicability of the SIM image in scenes such as biological imaging and tissue analysis are improved.

The invention relates to the technical field of image encryption, and discloses an image steganography method and system based on dynamic latent variables and frequency domain embedding, and the method comprises the steps: obtaining a hash value and a plurality of secret keys of a to-be-encrypted color plaintext image; constructing a hyperchaotic system, and iteratively generating a plurality of key streams; a to-be-encrypted color plaintext image is decomposed into R, G and B channel matrixes, and three ciphertext sequences are obtained based on the three channel matrixes; the method comprises the following steps: initializing a noise image, determining a time step by using a secret key, and executing reverse denoising of a denoising diffusion implicit model (DDIM) with a fixed step length to obtain an intermediate latent variable; based on the intermediate latent variable and the three ciphertext sequences, obtaining a dense frequency domain-containing sub-band, and performing inverse discrete wavelet transform on the low-frequency sub-band and the dense frequency domain-containing sub-band to obtain a dense latent variable; and executing DDIM denoising of the time step by taking the secret-containing latent variable as a starting point to obtain a secret-containing image. By adopting the method, the security and visual quality of image steganography can be improved, and high embedding capacity is ensured.

The invention discloses a zero-sample low-light image enhancement method based on joint frequency domain prior guide diffusion, which relates to the field of optics and comprises the following steps: decomposing the frequency domain of a low-light image; performing forward diffusion and reverse sampling processing on the low-light image by using a Markov chain structure model, and supervising an optimization processing result in combination with a multi-modal text to obtain a sampling result; decomposing the frequency domain of the sampling result to obtain a sampling low-frequency domain, and performing conversion processing on the sampling low-frequency domain and the low-light low-frequency information by using Fourier transform to respectively obtain amplitude and phase information of the sampling low-frequency domain and the low-light low-frequency information; and in combination with the amplitude and phase information of the sampling low-frequency domain and the low-light low-frequency information and the low-light high-frequency information, on the basis of inverse fast Fourier transform and inverse discrete wavelet transform, an image enhancement output is obtained by updating a sampling result. According to the method, the problem of poor image quality of the low-light image is solved by combining the frequency domain prior guide diffusion method.

The present invention relates to a method and device for active defense against deep fake face based on discrete wavelet transform, and to the field of image processing technology. The method comprises: using a deep fake model to edit the attributes of the original and disturbed images, calculating the mean square error loss and structural similarity loss between the two, and merging them into a total loss function after assigning different weights; obtaining gradient information based on the back propagation of the loss function, determining the optimal direction of the disturbance application, and forming an intermediate disturbance value; embedding low-frequency and high-frequency components into the corresponding components of the disturbed image through discrete wavelet transform, and then reconstructing the image through inverse discrete wavelet transform; checking whether the number of iterations has been reached, if not, introducing a JPEG noise layer to continue iteratively adjusting the disturbance; if so, outputting a face image containing invisible disturbance information. The present invention makes full use of the robustness in the wavelet domain, and improves the resistance of disturbance to JPEG compression while ensuring that the disturbance is invisible.

The invention discloses an offshore scene segmentation method based on condition invariant semantic segmentation, and belongs to the field of computer vision and semantic segmentation. According to the method, style mutual conversion of a source domain image and a target domain image is realized through a data loading stage, and a multi-style view pair is generated; a CISS network model is constructed, the CISS network model is composed of a MiT-B5 encoder and a context sensing decoder, the encoder extracts high-level features insensitive to visual conditions by means of discrete wavelet transform, and the decoder achieves feature reconstruction without information loss through inverse discrete wavelet transform and generates a category probability graph; designing a joint loss function including pixel-level cross entropy loss and feature-level invariance loss, and balancing loss weight in combination with a dynamic weighting strategy; and training and optimizing the model by adopting an AdamW optimizer in combination with a linear learning rate warm-up strategy. Experiments show that the method can effectively cope with visual condition changes in a real marine environment, and the robustness of the semantic segmentation model in different marine environments is improved.

The invention discloses an image processing method fusing frequency perception and a Mama network, and the method comprises the steps: collecting original data, carrying out the preprocessing, obtaining a sample set, training a constructed deep learning network, processing an input image in real time through the trained deep learning network at an online stage, and achieving a rainwater removal effect. On the basis of a fusion frequency perception and Mama network architecture, on one hand, discrete wavelet transform down-sampling operation, convolution operation, two wavelet domain recovery module feature extraction operation, convolution operation, two wavelet domain recovery module feature extraction operation, convolution operation and discrete wavelet inverse transform operation are sequentially carried out on a raindrop picture; and on the other hand, a high-frequency component is extracted from frequency information after wavelet transformation, and after frequency enhancement, a wavelet domain recovery module is sequentially guided to perform refined texture reconstruction, so that the rain removal effect is improved, and an image restoration result with higher quality is obtained.

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 invention relates to the technical field of smart power grids, and provides a subsynchronous oscillation suppression method and system for a mine mining area power supply system, and the method comprises the steps: collecting different types of electric data of each electric shovel in the mine mining area power supply system, and obtaining an electric data sequence of each electric shovel; marking a target electric shovel, performing multilayer wavelet decomposition on an electric data sequence of the target electric shovel, obtaining all detail coefficients of each layer, and determining an adaptive adjustment threshold value of the detail coefficients of the same layer; determining a consistency characteristic value of each type of electric data of the target electric shovel, obtaining an adjustment value of a detail coefficient of each layer of the same type of electric data of the electric shovel, and obtaining a de-noised electric data sequence of the electric shovel according to the detail coefficient and inverse discrete wavelet transform; and separating subsynchronous oscillation fault data according to the de-noised electric data sequence to realize subsynchronous oscillation suppression of the mine field mining area power supply system. The subsynchronous oscillation suppression effect of the power supply system can be improved.

The invention provides a hidden backdoor attack method based on dynamic mask and multi-level feature fusion, and relates to the field of deep neural network security, and the method specifically comprises the following steps: selecting a trigger graph, and carrying out the zooming processing of the trigger graph; performing three-level discrete wavelet transform on the clean image, performing wavelet transform on the trigger image, and extracting high-frequency information; the high-frequency component of the trigger image is embedded into the multi-level wavelet transform high-frequency part of the clean image through linear combination, and a poisoning image is generated through cubic inverse discrete wavelet transform; and optimizing a poisoning strategy by using a backdoor random masking strategy and a weak trigger training and strong trigger attack strategy. According to the technical scheme, the problems of low concealment and effectiveness of backdoor attacks in the prior art are solved.

The invention provides a signal-to-noise ratio evaluation method, system and device of an image, a medium and a program product. The signal-to-noise ratio evaluation method comprises the steps of obtaining an image of magnetic resonance function imaging before denoising; performing discrete wavelet transform on the image of the magnetic resonance functional imaging before denoising to obtain a high-frequency coefficient matrix and a low-frequency coefficient matrix; performing de-noising and discrete wavelet inverse transformation processing on the high-frequency coefficient matrix and the low-frequency coefficient matrix by using a soft threshold function to obtain a de-noised magnetic resonance functional imaging image; and acquiring the signal-to-noise ratio of the magnetic resonance functional imaging based on the image of the magnetic resonance functional imaging before denoising and the image of the magnetic resonance functional imaging after denoising. According to the invention, the automatic and precise evaluation of the signal-to-noise ratio of the magnetic resonance functional imaging image is realized through the discrete wavelet transform method, and the evaluation efficiency and accuracy of the signal-to-noise ratio are improved.

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 present invention discloses a personalized model copyright protection method and system based on DWT, which relates to the field of computer science and technology, including: using a generation network G to generate a personalized specific pattern for an original image. Then, the original image and the specific pattern are converted into low-frequency and high-frequency areas respectively through DWT. Then, the high-frequency information of the specific pattern is embedded into the wavelet domain of the clean image as a watermark trigger, and the combined image is converted from the wavelet domain to the spatial domain through the inverse discrete wavelet transform IDWT, and finally a watermark sample is generated. Starting from the characteristic attributes of the sample, the present invention uses a feature extractor to calculate the cosine similarity between the watermark sample and the original sample on the basis of ensuring the diversity of the watermark sample set, and selects samples with high contribution to the watermark model, so as to make the watermark verification process more efficient and accurate.

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 discloses a Beidou positioning power frequency harmonic interference suppression method and system and a medium, and the method comprises the steps: receiving a mixed radio frequency signal, and carrying out the down-conversion of the mixed radio frequency signal to obtain an analog intermediate frequency signal; converting the analog intermediate frequency signal into a digital intermediate frequency signal sequence; performing multi-layer discrete wavelet transform on the digital intermediate frequency signal sequence to generate an approximate coefficient sub-band and a detail coefficient sub-band; calculating the energy of the wavelet coefficients in all the sub-bands, comparing the energy with an adaptive energy threshold, and identifying an interference dominant sub-band; a self-adaptive threshold function is used for wavelet coefficients in the interference dominant sub-band so as to suppress interference components in the wavelet coefficients, and a processed wavelet coefficient set is obtained; performing inverse discrete wavelet transform on the processed wavelet coefficient set to obtain a reconstructed digital intermediate frequency signal sequence; and performing baseband processing on the reconstructed digital intermediate frequency signal sequence to obtain a Beidou positioning radio frequency signal after interference suppression. According to the invention, continuity and accuracy of positioning in a complex electromagnetic environment can be guaranteed.

The invention discloses a DWT-based personalized model copyright protection method and system, and relates to the technical field of computer science, and the method comprises the steps: generating a personalized specific mode for an original image through a generative network G; then, the original image and the specific mode are respectively converted into a low-frequency region and a high-frequency region through DWT (Discrete Wavelet Transform). Then, high-frequency information in a specific mode is used as a watermark trigger to be embedded into a wavelet domain of the clean image, the combined image is converted into a spatial domain from the wavelet domain through inverse discrete wavelet transform (IDWT), and finally a watermark sample is generated; according to the method, starting from the feature attributes of the samples, on the basis of ensuring the diversity of the watermark sample set, the feature extractor is adopted to calculate the cosine similarity of the watermark samples and the original samples for screening, and the samples with high contribution degree to the watermark model are selected, so that the watermark verification process is more efficient and accurate.

The present invention discloses a method for generating an SAR interferogram based on L<subgt;1 / 2< / subgt>-norm regularization, specifically as follows: Step 1: Use SAR to obtain the main and secondary SAR images of the same target scene, and obtain the phase of the secondary SAR image by taking the phase value of the complex matrix of the secondary image; Step 2: Based on the interferometric phase of the main and secondary SAR images, and establish a generation model of the SAR interferogram by solving the L<subgt;1 / 2< / subgt>-norm regularization problem; Step 3: Use the threshold iteration algorithm to solve the L<subgt;1 / 2< / subgt>-norm regularization problem to obtain a wavelet coefficient matrix related to the restored interferogram; Step 4: Perform an inverse discrete wavelet transform on the wavelet coefficient matrix obtained in Step 3, and take the transformed angle value as the restored interferogram. The interferogram reconstructed by the present invention has better performance and smaller phase noise; the quality of the SAR interferogram obtained by the present invention is higher.

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.

The application provides a wavelet transform-based atmospheric data fusion method, and relates to the field of satellite data processing. The method takes FY-4B data as a unified space-time base plate, performs two-dimensional discrete wavelet transform on the spatially overlapped regions of FY-4B and FY-3 series data to obtain wavelet coefficients of each scale of each satellite data; performs time enhancement processing on the wavelet coefficients of each scale of the FY-4B data, and performs inverse discrete wavelet transform on the wavelet coefficients of each scale of each satellite data to reconstruct each satellite image signal; calculates the comprehensive scores of each satellite data to determine the initial fusion weight, and corrects the final fusion weight through an intelligent credibility switching mechanism; and performs weighted summation on each satellite image signal according to the final fusion weight to generate a surface reflectivity product. The application can fuse the complementary advantages of geostationary satellites and polar-orbiting satellites, and improve the spatio-temporal resolution and detail fidelity of multi-source satellite data fusion.

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.