20 results about "Residual noise" patented technology

Proposed concepts aim to provide schemes, solutions, concepts, designs, methods and systems pertaining to the storage, retrieval and / or communication of medical images. In particular, the medical image is de-noised to generate a (substantially) noise-free medical image. De-noising the medical image to this extent allows for a high compression ratio, and thus space-efficient storage of the medical image. However, typically the level to which medical image is de-noised has to be carefully controlled in order to minimise the chance of introducing errors to ratio the medical image. In the present invention, noise information describing residual noise data removed from the medical image is also stored. This allows for the compressed noise-free medical image to be combined with the compression noise information at a receiver, restoring any potentially lost information. In this way, a space-efficient means for storing a medical image is provided, which also overcomes problems associated with heavily de-noising the medical image.

The application discloses a video human behavior prediction method based on residual diffusion theory and skeleton points, comprising the following steps: S10, acquiring an original group motion sequence, decomposing the multi-person sequence into individual trajectories, and then converting the time-domain motion sequence of each individual into frequency coefficients through discrete cosine transform (DCT); S20, based on the frequency domain representation, introducing a residual term to construct a forward diffusion process; S30, defining a weighted residual noise as a learning target, and realizing conversion from noise to residual noise; S40, determining an optimal step through transformation, realizing acceleration and unified training and reasoning; S50, applying physical constraints to single-person prediction, introducing a weighted combined biomechanical regularization loss function to constrain the physical feasibility of the generated sequence; and S60, synthesizing a group sequence and outputting a predicted sequence. The application utilizes deep learning technology, extracts human skeleton point information, combines a residual diffusion mechanism, predicts human behavior in a video sequence or a real-time scene, and realizes high-precision prediction of future behavior.

A noise cancellation system with secondary path adaptation, comprising: a noise cancellation filter configured to receive a reference signal representing a noise source within a predetermined volume and to generate a noise cancellation signal based at least in part on the reference signal, the noise cancellation signal, when transduced by a loudspeaker, produces a noise cancellation acoustic signal that reduces noise in a cancellation zone within the predetermined volume; a secondary path estimate filter configured to receive an input signal and to implement an estimate of a secondary path transfer function, the secondary path transfer function being a transfer function between the loudspeaker and the cancellation zone, the secondary path estimate filter outputs an output signal based at least in part on the estimate of the secondary path transfer function and the input signal; an adaptation module configured to adjust coefficients of the noise cancellation filter according to a first adaptation algorithm based at least in part on the estimated output signal; and a secondary path adaptation module configured to adjust coefficients of the secondary path estimate filter according to a second adaptation algorithm, wherein an adaptation rate of the second adaptation algorithm is based at least in part on a coherence between the reference signal and an error signal representing residual noise within the cancellation zone.

This disclosure provides an image noise processing method, apparatus, electronic device, and storage medium. The method includes: acquiring at least two first sampled images of the same scene; determining pixel values ​​of pixels in the at least two first sampled images; wherein the pixel values ​​include random values ​​indicating the influence of random noise and non-uniform values ​​indicating the influence of non-uniform noise; determining residual noise information based on the difference between the pixel values ​​of corresponding pixels in two second sampled images; wherein the first sampled images include the second sampled images; and acquiring noise parameters for noise reduction based on the residual noise information.

This utility model relates to the field of audio acquisition equipment technology and discloses a microphone for classrooms, including a microphone body comprising a housing. This microphone for classrooms constructs a highly efficient dual noise reduction system by incorporating a noise-reducing cover and composite sound-absorbing cotton. The noise-reducing cover tightly wraps around the housing, utilizing its material properties to block most external environmental noise, such as noise from outside the window and footsteps outside the classroom. The composite sound-absorbing cotton, filled between the housing and the noise-reducing cover, further absorbs residual noise penetrating the cover while reducing noise generated by vibrations of internal components. This design allows the microphone to effectively filter out non-target sounds in complex classroom environments, significantly improving the clarity of target sounds such as teachers lecturing and students answering questions. It avoids the audio blurring problem caused by noise interference in traditional microphones, providing higher-quality sound materials for classroom recording, remote teaching, and other scenarios.

This disclosure provides a method for locating and measuring the height of a display panel ink-coated barrier, relating to the display field. The method includes: acquiring an original barrier region image including an inner barrier and an outer barrier; performing anti-interference preprocessing on the original barrier region image using an anti-interference preprocessing module to obtain a preprocessed barrier region image, which includes: the anti-interference preprocessing module using db wavelet decomposition to zero high-frequency fringe coefficients and then inverse transforming to suppress periodic interference fringes; the anti-interference preprocessing module using variable window adaptive median filtering to remove residual noise and display substrate scratch images; inputting the preprocessed barrier region image into an improved Unet network to obtain pixel-level coarse boundaries of the barrier; and locating the pixel-level coarse boundaries of the barrier based on a camera calibration module to locate the sub-pixel-level boundaries of the inner and outer barriers, thereby improving the anti-light source interference capability during the positioning process of the display panel ink-coated barrier.

This invention provides an audio processing circuit for generating an output signal for an audio function. The audio processing circuit includes at least one adaptive filter and a leak detection circuit. The at least one adaptive filter controls the output signal. The leak detection circuit performs leak detection based on a first input signal and a second input signal, and adjusts the at least one adaptive filter based on a leak detection result. The first input signal indicates noise signal characteristics. The second input signal originates from an error signal obtained by an error microphone picking up residual noise generated by the audio function.

This invention relates to the field of audio signal processing technology, specifically to a directional sound pickup and adaptive noise reduction system and method using a multi-microphone array. The invention acquires signals through a multi-microphone array and performs time-frequency conversion. A beamforming unit forms a directional beam according to the target direction and constructs spatial nulls in the interference direction. A noise reduction unit adaptively suppresses noise in the beam output, simultaneously extracting residual noise energy and speech distortion feature values. A closed-loop feedback control unit executes a zero-point adaptive optimization algorithm based on the joint constraints of residual noise energy and speech distortion. Using the aforementioned feature values, a cost factor is calculated and a beam zero-point direction update is generated, which is fed back to the beamforming unit to achieve closed-loop adaptive adjustment of the null direction. This breaks the limitation that traditional beamforming and post-noise reduction are independent, and can simultaneously improve noise suppression capability and speech fidelity in complex acoustic environments.

This invention relates to the field of image enhancement technology and discloses a low-light image enhancement method and system for 5G remote control systems based on deep Retinex decomposition. The method acquires and preprocesses the original low-light image from the front-end device of the 5G remote control system, inputs it into a lightweight multi-scale illumination sensing network to separate the initial illumination and reflection components, corrects the initial illumination component using a dynamic dual-branch exposure fusion module, processes the initial reflection component using a residual noise-detail co-optimization module, and then reconstructs and enhances the image through a halo and color joint correction mechanism. The image is then encoded, compressed, and transmitted to the remote control center via a 5G private network. This invention solves the core problems of underexposure and overexposure, noise interference, detail loss, and wasted bandwidth in low-light images in 5G remote control scenarios, achieving lightweight real-time enhancement at the edge, ensuring image quality and transmission efficiency, and providing reliable support for remote control decisions.

The application discloses a subcutaneous speech enhancement method based on SATF estimation and stream matching, which constructs a simulation adaptive framework containing SATF estimation and stream matching noise simulation double branches, performs normalized cross-correlation pre-alignment on a subcutaneous speech pair and executes an STFT transformation, constructs an SATF library through STFT domain least square estimation and frequency smoothing, and synthesizes band-pass channel colored simulation speech based on the SATF library and dynamic noise; scene-related structured residual noise is generated by combining stream matching, simulation speech and structured residual noise are fused to obtain final simulation noisy speech, and the final simulation noisy speech is paired with clean speech to generate aligned simulation training speech pairs, and a pre-training speech enhancement model is adaptively trained by using the training speech pairs. The application can effectively alleviate the problems of subcutaneous speech data scarcity and alignment deviation, improve speech enhancement effect, and adapt to applications of full-implantable cochlear implants and other implantable hearing systems.

The invention relates to a method (100) for automatically detecting digital forgery for the purpose of verifying the conformity of a digital copy (DD) of an official document (OD). Such a method (100) primarily comprises a step (130) of processing one or more images (DF1, …, DFj, …DFm) extracted from the digital copy (DD) in order to produce one or more images (DF1', …, DFj', …DFm') expressing a residual noise, and a step (140) of analysing this residual noise in order to produce a conformity result (SCj, ASC) in relation to the digital copy (DD). Such a method (100) is designed to be implemented by the processing unit of an electronic entity (20) for verifying such conformity, possibly in response to a request from a third-party electronic entity (40) operated by a state or service provider.

This invention provides a method for generating raw domain noise-paired data under extremely low light conditions, comprising: S1, collecting noise modeling data and calibrating noise parameters; S2, collecting and creating clean data; S3, designing a random brightness mapping strategy; S4, designing mosaic data augmentation; S5, designing flip-rotation data augmentation; S6, generating noise data; S7, designing a color distortion perturbation strategy; S8, during training, executing steps S3 to S7 sequentially, resulting in the final noise data I. N2 and normalized data I norm This method constructs paired data. Based on existing noise modeling methods, it processes easily obtainable clean data to avoid the cumbersome generation process of real paired data in extremely low light scenes and the possible problems of pseudo textures, residual noise, and color bias. At the same time, it uses data augmentation to expand the diversity of data and improve the generalization ability of the model.

The application relates to the technical field of concrete ultrasonic flaw detection image processing, and discloses a concrete flaw detection ultrasonic image speckle noise suppression method and system based on multi-modal signal processing, which is characterized in that the original ultrasonic signal is subjected to adaptive white noise injection and EMD decomposition, non-aliasing IMF components are obtained through modal alignment and averaging, and high-frequency noise dominant components and medium-low-frequency effective signal components are divided according to the kurtosis criterion; subsequently, the high-frequency components are subjected to VMD optimization, the frequency band is accurately divided by constructing a variational constraint model and iteratively solving; the improved adaptive wavelet threshold function and the hierarchical hybrid threshold strategy are adopted for the medium-low-frequency components, so that the noise is accurately filtered out and the effective signal is retained; finally, the de-noised components of various frequency bands are fused to generate a one-dimensional time domain signal, the residual noise points are removed through median filtering after being restored into a two-dimensional image, and the de-noised enhanced image is output. The application realizes accurate separation of the noise and the effective signal, and completely retains the fine defect features in the concrete.

The invention relates to an audio processing method, device and system, electronic equipment and a storage medium, and the method mainly comprises the steps: obtaining a first audio signal which is an audio signal obtained after an original audio signal is subjected to primary noise reduction and amplification; performing first residual noise detection on the first audio signal to obtain first detection result information associated with the first residual noise detection; under the condition that the first detection result information meets a preset secondary noise reduction and amplification starting condition, performing secondary noise reduction and amplification processing to obtain a second audio signal; and outputting the second audio signal. According to the technical scheme, whether secondary noise reduction amplification is started or not is determined through residual noise detection of the audio signal after primary noise reduction amplification, adaptive noise reduction amplification of the audio signal collected in a complex environment is achieved, and the voice definition degree of the audio signal collected in various environments can be improved.

The application discloses a kind of based on noise curve and volume feedback's automobile volume adjusting method, comprising the following steps: noise equation data acquisition, the size of environmental noise brought by each noise factor in different states of test car is tested, and the equation capable of reflecting noise size is obtained from test result;The value of real-time detection noise factor is brought into the equation obtained in step one, and the sound volume is adjusted after obtaining noise data at this time;Again adjustment, the sound data is obtained by real-time sound receiving in vehicle, and the sound spectrum is obtained after Fourier transform is carried out to sound data, inverse Fourier transform is carried out after the sound of sound playing part is eliminated in frequency domain, and the volume of residual noise in time domain is obtained, compared with the time domain volume of direct sound receiving before processing, and the volume of vehicle sound is adjusted appropriately, so that the hearing volume of sound in vehicle compartment is more uniform, the frequency of driver adjusting volume is reduced, and the attention of driver is avoided to be dispersed.

This invention discloses a method for restoring severe weather images based on cross-frequency collaborative guided diffusion in the wavelet domain. The method first constructs residual information based on the difference between the degraded input image and the corresponding clear image, generating a residual noise diffusion state image. Then, a bi-branch diffusion denoising network is constructed to characterize the degraded image restoration process, collaboratively modeling structural and detail information in the diffusion state image and outputting the residual prediction results corresponding to each wavelet sub-band. High-frequency branch features are used to guide the modulation of the low-frequency branch to enhance structural restoration capability. Furthermore, cross-frequency feature fusion is performed on the prediction results of each frequency band to achieve information interaction between different frequency bands. Finally, the restored clear image is obtained through inverse wavelet transform and inverse diffusion. This invention can achieve collaborative restoration of structural and detail information in degraded severe weather images, improving image restoration quality, and is applicable to de-raining, de-snowing, de-fogging, de-raindrop, and other degraded image restoration scenarios.

The present application relates to the field of intelligent operation and maintenance of hydroelectric power generation and active acoustic control, and discloses a method and system for intelligent acoustic impedance matching of a hydraulic turbine based on deep learning. The method comprises: collecting full-flow multi-source heterogeneous data to construct a sensor topology graph; using a spatiotemporal graph neural network for feedforward prediction of flow field excitation characteristics; using a multi-agent deep reinforcement learning algorithm with integrated physical constraint learning to make decisions on optimal configuration instructions; actively canceling intrinsic capacitance through a negative impedance converter, reconstructing the equivalent mechanical stiffness of the superstructure, and realizing real-time matching and noise reduction of acoustic impedance; and performing online fine-tuning based on residual noise feedback. The system comprises perception, reasoning, decision-making, execution and feedback optimization modules. The present application breaks through the physical limitations such as the Pashen limit through a non-Foster circuit, significantly enhances the suppression effect of ultra-low frequency below 100 Hz, effectively solves the control lag problem through feedforward logic, improves the stability of complex working conditions, and ensures the adaptability and robustness throughout the life cycle by combining physical constraints and incremental learning.

The present disclosure provides a video generation method, device, equipment, medium and product, relating to the technical field of artificial intelligence. The method comprises: inputting a random noise latent code and a target text into a diffusion model to denoise the random noise latent code by taking the target text as semantic guidance, to obtain an initial latent code; performing time dimension expansion and spatial super-resolution reconstruction on the initial latent code respectively to obtain an intermediate latent code; inputting the intermediate latent code and the target text into the diffusion model to determine residual noise, to correct the intermediate latent code based on the residual noise to obtain a target latent code, wherein the residual noise is used to represent the semantic difference between the intermediate latent code and the target text; and mapping the target latent code to a pixel space to generate a target video corresponding to the target text.

This utility model discloses a dual-feed over-ear noise-canceling headphone, relating to the field of over-ear noise-canceling headphone technology. It includes an earphone shell, with a speaker mounting shell internally connected to it. A speaker is mounted on the right side of the speaker mounting shell, and a silicone pad is attached to the outer side of the speaker mounting shell. A bracket is rotatably connected to the outside of the earphone shell, and an extension bracket is slidably connected to the inner side of the bracket. A noise-canceling component is mounted on the other side of the speaker mounting shell. This dual-feed over-ear noise-canceling headphone uses a feedforward microphone and a feedback microphone to collect external ambient noise and residual noise within the earcups, respectively. The earphone shell also houses a speaker for playing audio signals and processed noise-canceling signals. The headphone includes a noise-canceling control circuit, including a feedforward filter and a feedback filter, which process the noise signals collected by the feedforward and feedback microphones, respectively.

This invention discloses a method for denoising sensor data in flight control systems under electromagnetic interference environments, belonging to the field of flight control system signal processing technology. The method includes: acquiring a flight control sensor noise dataset and dividing it into a training subset and a real-time processing subset; extracting time-domain and frequency-domain features of each channel and associating them with interference intensity signal features to generate noise feature vectors; constructing a hybrid noise recognition model to identify noise patterns; performing hierarchical misjudgment correction through cosine similarity matching; marking signal segments suspected of residual noise and establishing a noise interference mapping table; performing differentiated denoising processing on marked signal segments and normal signal segments; performing secondary denoising on signal segments suspected of residual noise; collecting denoised sensor data and calculating three-dimensional indicators to evaluate the denoising effect; if the indicator meets the standard, verifying the improvement in flight control accuracy; if the indicator does not meet the standard, adjusting the parameters of the hybrid noise recognition model and iteratively optimizing it until the standard is met, thus achieving high-precision identification and adaptive denoising of flight control system noise under electromagnetic interference environments.