12results about How to "Avoid artifacts" patented technology

The application discloses a kind of autonomous ultrasonic robot multimode drag control method and device.The method comprises: obtaining real-time ultrasound image and the force data value of mechanical arm end;Determine whether the pixel value of ROI region of the ultrasound image of continuous preset frame number is greater than preset pixel threshold value: if yes, according to force data value and first initial control parameter, it is calculated to obtain the target displacement amount in X axis direction and Y axis direction, simultaneously, according to force data value, preset expected force and second initial control parameter, it is calculated to obtain the target displacement amount in Z axis direction;Tool coordinate system under target displacement amount is converted into world coordinate system under target displacement amount;Zero force control is implemented in the horizontal direction of probe contact surface, and constant force control is implemented in the vertical direction of probe contact surface, until probe moves to target position.The method improves the operation flexibility and scanning safety by the synergistic effect of zero force control and constant force control.

This invention discloses a multi-channel electromagnetic induction detection method and system based on uniform magnetic field constraint. A uniform magnetic field is constructed within the detection area of ​​the structure under test. Electromagnetic induction response signals are acquired through multiple channels under the constraint of the uniform magnetic field, and the spatial coordinates of each acquisition channel are marked. The raw electromagnetic induction signals acquired by each channel are preprocessed, including noise reduction filtering, envelope extraction, and amplitude normalization. A deep learning model is then used to extract structural features from the preprocessed multi-channel electromagnetic response signals. Based on the spatial position information of each acquisition channel, magnetic field uniformity parameters, and response characteristics, a channel correlation weight matrix is ​​constructed to describe the spatial consistency and physical coupling relationships between channels. A multi-channel joint optimization model is constructed, and the equivalent electromagnetic parameter distribution inside the structure under test is reconstructed through iterative solution. This invention improves the reliability, repeatability, and engineering applicability of electromagnetic induction detection results.

This application discloses an image processing method and apparatus in the computer vision field of the artificial intelligence field. The image processing method includes: obtaining a channel image corresponding to a to-be-processed raw (raw) image, where the channel image includes at least two first channel images; performing image restoration processing on the first channel image to obtain the restored first channel image; and performing image restoration processing on the to-be-processed raw image based on the restored first channel image to obtain a raw image obtained through joint demosaicing and denoising processing. The technical solution of this application can improve image quality of the raw image obtained through joint demosaicing and denoising processing.

This invention discloses a target trajectory background modeling method and system based on bidirectional difference and grayscale correction, belonging to the field of image data processing technology. The method includes: S1, periodically acquiring basic image trajectory data and performing preprocessing on the acquired data; S2, constructing a frame index sequence, generating a frame pairing table and a grayscale coefficient table, performing grayscale correction and bidirectional difference calculation on the paired frame sequence to obtain the candidate motion mask for the current frame; S3, obtaining the initial background region of the current frame, evaluating the background confidence level of each pixel, and generating an effective background region; S4, comprehensively evaluating the stability level and fluctuation constraint capability of the background, dividing the stable and weak background regions, and outputting the frame-level motion mask to complete the background closed-loop output. This solves the problem that traditional background modeling methods directly use arithmetic mean modeling in a few-frame sequences, leading to the background grayscale being lowered by high-contrast moving pixels, thus generating motion artifacts and causing weak target detection failure.

The invention relates to an image classification high-concealment back door sample generation method in combination with space-frequency domain features, and belongs to the field of artificial intelligence safety. The method comprises the following steps: firstly, extracting a high-frequency component of a trigger by using wavelet transform, embedding inverse transformation information of the high-frequency component into an original image, and converting into an initial trigger image spectrum; extracting phase spectrums and amplitude spectrums of the trigger and the original image by using FFT, and recombining the phase spectrums of the trigger and the amplitude spectrums of the original image to generate a reconstructed trigger image spectrum; performing linear addition and inverse transformation on the initial trigger image spectrum and the reconstructed trigger image spectrum to generate a primary trigger image; and finally, taking the original image and the primary trigger image as training samples, and training by using a conditional GAN model to generate a back door sample generator. Aiming at the problem that the concealment of the backdoor sample is influenced by image blurring and distortion easily caused by embedding trigger full-frequency information into an original image in the existing method, the attack success rate of the backdoor sample is effectively improved by using trigger high-frequency characteristics and original image spatial domain characteristics as supervisory information training models.

The application discloses a video processing method, device and equipment and a computer readable storage medium, and applies to the field of video processing, and comprises the following steps: receiving a code stream output by an encoder; if a saturation enhancement enabling flag in the code stream is valid, determining whether a pixel belongs to a skin color protection area according to a preset condition in a linear light domain; if the pixel belongs to the skin color protection area, performing smoothing interpolation processing on a saturation scaling factor in metadata parameters by using a sliding window according to a relative position of a hue angle of the pixel in a skin color interval to obtain an effective enhancement coefficient; if the pixel does not belong to the skin color protection area, taking the saturation scaling factor as the effective enhancement coefficient; and obtaining an enhanced color component according to a color difference component of the pixel and the effective enhancement coefficient and a luminance value of the pixel. The application solves the problems of insufficient saturation, over-saturation of the skin color area, skin color distortion and other video quality problems of an HDR video after dynamic range conversion.

The application discloses a video stabilization method and system based on video inter-frame homography transformation, belongs to the technical field of video supervision, and effectively distinguifies foreground and background in a video through a foreground mask module; after the foreground part is covered by using the mask, effective shallow semantic features of a background area are acquired through a credible shallow feature extraction module; for the effective shallow features of the background area, a motion estimation module associated with local features and global features is used to estimate the homography relationship between continuous adjacent frames; through a video stabilization smoothing module, the homography between the transformation frame and the reference frame obtained by using the motion relationship between adjacent frames is used to realize the stabilization of the whole video by using STN transformation. The effective combination of the foreground mask and the credible shallow feature avoids the artifact or distortion phenomenon caused by large disparity, the local and global feature relationship is established based on the credible shallow feature, accurate camera motion is obtained, and finally, the video stabilization is realized.

The utility model relates to medical anti -counterfeit image neck cover technical field, and disclose a kind of adjustable pressure type inflation anti-counterfeit image neck cover device, including pneumatic support assembly, the quantity of pneumatic support assembly is two groups, pneumatic support assembly includes telescopic air bag and two groups of plastic supporting block;Inflation and deflation component, the inflation and deflation component includes air pipe and micro dual-purpose air pump;Neck wear, the both ends of neck wear are respectively connected with corresponding plastic supporting block fixedly;Guiding component, the guiding component is fixedly installed between two groups of plastic supporting block, and guiding component includes support sleeve and cylindrical rod, and compression spring is arranged in support sleeve cavity. By setting telescopic air bag between two groups of plastic supporting block, by micro dual-purpose air pump cooperation air pipe to telescopic air bag inflation and deflation, telescopic air bag inflation when bulging will two groups of plastic supporting block be mutually far away, to support between the head of patient and shoulder, effectively reduce the neck shaking amplitude caused by breathing, to avoid the appearance of CT artifact.

The invention discloses a composite material ultrasonic defect burial depth detection and imaging method, which belongs to the technical field of nondestructive testing, and comprises the following steps: collecting an A scanning signal of a composite material test piece through a water immersion type focusing probe; performing continuous wavelet transform and dynamic threshold noise reduction on the signal; constructing a particle swarm optimization wavelet transform modulus maximum (PSO-WTMM) model, and searching an optimal parameter by using a fitness function of envelope entropy fusion peak value penalty; extracting the echo time difference of the upper and lower surfaces based on the optimal parameters, and calculating the defect burial depth in combination with the sound velocity; and constructing a time difference and amplitude ratio matrix through point-by-point scanning, and outputting a C-scan image after interpolation enhancement. By adopting the composite material ultrasonic defect burial depth detection and imaging method, the defects that parameters depend on experience and the robustness is poor in a traditional method are overcome, the defect depth positioning precision and the imaging quality are improved, and the method is suitable for nondestructive detection and health monitoring of aviation composite material structures.

The application discloses a kind of based on degradation perception's fundus image quality enhancement method, system, equipment and medium, wherein method includes: to original low-quality fundus image is standardized pretreatment;To the fundus image after pretreatment is divided into area, feature extraction and degradation degree evaluation, and introduce the self-attention mechanism with degradation perception penalty term, realize differentiation feature enhancement;Build generation network, including encoder and decoder, finally output enhanced fundus image;Build discriminant network, including multiple parallel multi-scale convolution branches, the output of multiple branches is fused, and through double output head respectively outputs true probability value and authenticity confidence heat map;The generation network and discriminant network are trained against;For the fundus image to be enhanced, after image pretreatment and local feature self-adapting enhancement, input trained generation network, output quality enhanced fundus image.The application can realize high-precision, high-fidelity enhancement to low-quality fundus image.

The invention provides an underground cable three-dimensional imaging method based on X-ray data, and the method comprises the steps: obtaining the X-ray data used for detecting an underground cable, and determining the compressed projection data and an initial attenuation coefficient field of the underground cable according to the X-ray data; based on the compressed projection data, the initial attenuation coefficient field and a preset objective function, the deformation field and the attenuation coefficient field of the underground cable are iteratively updated until the attenuation coefficient field of the underground cable converges, a target attenuation coefficient field is obtained, the objective function is used for realizing sparse reconstruction through cable geometric priori constraint, and in each iteration, the deformation field of the underground cable and the attenuation coefficient field of the underground cable are subjected to sparse reconstruction. Performing parameter optimization by using the updated attenuation coefficient field in the current iteration round; and based on the target attenuation coefficient field and the updated deformation field in each iteration round, performing three-dimensional model fusion to obtain a three-dimensional image of the underground cable. Therefore, efficient, accurate and nondestructive detection of the underground cable can be realized.

The invention relates to a compressed domain weak light image enhancement method and system based on 2D Gaussian splashing, and belongs to the technical field of image processing and computer vision. According to the method, an input weak light image is converted into discrete geometric primitive representation by using a 2D Gaussian splash compression reconstruction strategy, and explicit parameterization of the image is realized by optimizing position, covariance, color and opacity parameters; the method comprises the following steps: constructing a semantic-guided hybrid expert enhancement network model, extracting multi-scale semantic features by using a lightweight encoder, generating a spatial hybrid weight, and guiding a group of learnable color transformation operators to perform residual color modulation on Gaussian primitives; and then constructing a multi-target unsupervised loss function, obtaining an enhanced Gaussian parameter through iterative optimization, and rendering a normal illumination image. According to the method, the strategy of'compression, namely enhancement 'is put forward for the first time, calculation redundancy and secondary distortion caused by pixel domain processing are effectively avoided, and high-quality weak light image enhancement is achieved while the high compression rate is kept.