154 results about "Domain transformation" patented technology

A process that assists with the identification of potential hydrocarbon deposits that includes performing a structural interpretation of a three-dimensional seismic volume, transforming the three-dimensional seismic volume into a stratal-slice volume, performing a stratigraphic interpretation of the stratal-slice volume which includes the extracting of bounding surfaces and faults and transforming the stratal-slice volume into the spatial domain. As illustrated in FIGS. 24a, b and c, an exemplary seismic volume before Domain Transformation is presented in FIG. 24a, interpreted horizons and faults used in the transformation are presented in FIG. 24b, and the Domain Transformed stratal-slice volume is presented in FIG. 24c. The input seismic volume in FIG. 24a has deformations associated with syn- and post-depositional faulting. The output Domain Transformed volume (FIG. 24c) is substantially free of deformations.

Provided is a highband coding apparatus and method for a wideband coding system. The coding apparatus and method can reduce a pre-echo phenomenon by encoding the highband based on lowband encoding information and Temporal Noise Shaping technique. A highband encoding apparatus includes: a domain converter for converting the domain of an input highband signal into a frequency domain; a linear prediction order determiner for determining a linear prediction order based on the lowband encoding information; a linear prediction analyzer for analyzing a highband signal of the frequency domain based on the determined linear prediction order to thereby generate a linear prediction coefficient; a linear prediction coefficient quantizer for quantizing the linear prediction coefficient based on the lowband encoding information; and a residual signal quantizer for obtaining a residual signal by dequantizing the quantized linear prediction coefficient and quantizing the residual signal.

A steganographic digital watermark signal is decoded from host imagery without requiring a domain transformation for signal synchronization, thereby speeding and simplifying the decoding operation. In time-limited applications, such as in supermarket point-of-sale scanners that attempt watermark decode operations on dozens of video frames every second, the speed improvement allows a greater percentage of each image frame to be analyzed for watermark data. In battery-powered mobile devices, avoidance of repeated domain transformations extends battery life. A great variety of other features and arrangements, including machine learning aspects, are also detailed.

A process that assists with the identification of potential hydrocarbon deposits that includes performing a structural interpretation of a three-dimensional seismic volume, transforming the three-dimensional seismic volume into a stratal-slice volume, performing a stratigraphic interpretation of the stratal-slice volume which includes the extracting of bounding surfaces and faults and transforming the stratal-slice volume into the spatial domain. As illustrated in FIGS. 24a, b and c, an exemplary seismic volume before Domain Transformation is presented in FIG. 24a, interpreted horizons and faults used in the transformation are presented in FIG. 24b, and the Domain Transformed stratal-slice volume is presented in FIG. 24c. The input seismic volume in FIG. 24a has deformations associated with syn- and post-depositional faulting. The output Domain Transformed volume (FIG. 24c) is substantially free of deformations.

The invention relates to the field of image enhancement and discloses a Retinex-theory-based nonlinear image enhancement method and system. According to the method and the system, a gradient-domain-transformation-based edge-preserving filter manner is used for replacing the conventional Gaussian filter manner to obtain illumination components. On the one hand, the phenomenon of halo artifact near a high-contrast edge is effectively overcome; on the other hand, the edge-preserving filter of color images can be carried out in real time. In addition, a nonlinear compression function is constructed on account of the illumination components, a highlight area is suppressed and a dark area is effectively improved; the phenomenon of overexposure of the images is avoided; the image luminance is mapped to an ideal dynamic range; a self-adaptive local nonlinear enhancement manner is used for enhancing image details, so that the local details of reflection components are enhanced, the quality of the images is well improved; the enhanced images are closer to real natural sites; the details are more clear.

A process that assists with the identification of potential hydrocarbon deposits that includes performing a structural interpretation of a three-dimensional seismic volume, transforming the three-dimensional seismic volume into a stratal-slice volume, performing a stratigraphic interpretation of the stratal-slice volume which includes the extracting of bounding surfaces and faults and transforming the stratal-slice volume into the spatial domain. As illustrated in FIGS. 24a, b and c, an exemplary seismic volume before Domain Transformation is presented in FIG. 24a, interpreted horizons and faults used in the transformation are presented in FIG. 24b, and the Domain Transformed stratal-slice volume is presented in FIG. 24c. The input seismic volume in FIG. 24a has deformations associated with syn- and post-depositional faulting. The output Domain Transformed volume (FIG. 24c) is substantially free of deformations.

The invention discloses an image encryption method based on quantum chaotic mapping and fractional domain transformation. The method comprises the steps of firstly carrying out iterative scrambling on pixel points by use of Henon mapping; then multiplying a scrambled matrix and a row scrambling matrix to carry out x-direction and alpha-order DFRFT ((Discrete Fractional Fourier Transform); multiplying the transformed matrix and a column scrambling matrix to carry out y-direction and beta-order DFRFT; and finally carrying out a diffusion encryption operation on the transformed matrix by use of quantum Logistic chaotic mapping. According to the method, the defects of few parametric variables, simple system structure, pseudorandom and poor aperiodicity caused by the single use of a certain scheme such as spatial domain, transform domain and chaotic system in conventional methods are overcome, and experiment and simulation results show that the method has the safety higher than that of the conventional encryption method.

This invention relates to a network video encoder multidimensional code rate control method. The image control unit of an input video sequence containing macroblock strips, image frames or image set is divided into different dimensions of set spaces according to the time domain, space domain, time domain transformation, space domain, time domain transformation, space domain transformation and space-time domain transformation, a code rate control unit transmits the time-varying state of band-width, the occupation degree of a buffer zone and resource and process ability of multi-access terminal based on the multiple element isomeric network channels to carry out variable bit rate encode of the divided set space to match with the current video encoder resource, or network transmission code rate, the process ability of the receive end and the decoder, the video decoder reconstructs related multidimensional images to display image sequence based on the grammar ID plugged by received stream.

The invention relates to an image segmentation method having multi-layer segmentation networks based on Bayes framework with edge prior information. The method herein includes the following steps: S1.performing general classification on input data by using a full convolutional neural network, outputting scoring graphs of all types that have the same sizes of the input data, also extracting an implicit edge image from an internal feature layer of the full convolutional neural network; S2. extracting an explicit edge image from the input data by using an edge detection network; S3.performing first restraining on the types that are obtained from S2 by using domain transformation and conditional random field, obtaining an initial segmentation image; S4. transforming the explicit edge image obtained from S2 to an edge distance image; and S5. inputting the edge distance image to defined domain transformation, performing second edge restraining on the initial segmentation image that is obtained from S3, and obtaining a final segmentation result. According to the invention, the method can extract edge prior information through an external edge network and performs edge region segmentationand filtering on the result from the general segmentation by using the edge prior information, so that the method herein can increase the accuracy in segmenting a SAR image.

The invention belong to the technical field of communication and particularly relates to a low complexity three-dimensional (3D) beam forming algorithm based on angle-domain transformation. In allusion to single-cell multi-user-multiple input multiple output (MU-MIMO) downlink broadcast channel scenes, a 3D beam forming problem is modeled to form an optimization model of a minimum base station transmitting power, then the original problem is converted to an equivalence optimization problem on an angle domain through angle-domain transformation, the problem is subjected to dimensionality reduction relaxation by means of angle-domain sparse characteristics of 3D-MIMO channels, thereby, the calculating complexity of solution is reduced, and the algorithm complexity is never increased with increasing of the number of base station antennae. A simulation result shows that the angle-domain dimensionality reduction optimization algorithm can excellently approach original space all-dimension optimization algorithm.

The invention discloses a single-image self-adaptation defogging method based on domain transformation and weighted quadtree decomposition. The method comprises the following concrete implementation steps that a foggy image is obtained, the foggy image is subjected to RGB (red green and blue) three-channel minimum value calculation to obtain a dark primary color image of the foggy image, the brightness average values of the foggy image and the dark primary color image are weighted, and the fog concentration of the foggy image is judged according to the weighted value range; a weighted quadtree decomposition method is used for determining the atmosphere light vector value through selecting the region with the most concentrated fog from the dark primary color image; a coarse transmission rate is obtained according to the dark primary color image, and the transmission rate value in a similar part to the atmosphere light value is corrected; a filter based on domain transformation is used for filtering the corrected transmission rate function, and in addition, a primary defogging image is obtained according to an atmosphere scattering model; the foggy image and the primary defogging image are transformed to an L alpha beta space, L passages of the two images are subjected to weighted fusion and are transformed back to the RGB space to obtain the final defogging image. The defogging method provided by the invention has the advantages that the image definition in foggy weather is improved, and the processing speed is high.

The invention discloses a double-station frequency-modulation continuous wave synthetic aperture radar imaging method which particularly includes the steps of conducting dechirp processing on echo signals, removing residual video phases, conducting distance direction time-frequency replacement and azimuth Fourier transformation, conducting rough matching focusing to remove space invariant items of the phases, and conducting wave number domain transformation to remap the distance direction frequency. By means of the double-station frequency-modulation continuous wave synthetic aperture radar imaging method, instantaneous slant distance changes caused by continuous motion of a transmitting-receiving station within the pulse duration time are considered, Doppler parameters in echoes, distance migration and high-order coupling linear space variant characteristics are adopted, point-target echo two-dimensional frequency spectrum airspace linearization is achieved, and the point-target echo two-dimensional frequency spectrum airspace problem is solved.

The invention discloses a real-time color image filtering method based on nonlocal domain transformation. The method comprises the following steps: using a two-dimensional color image as a two-dimensional manifold on a five-dimensional vector space, and defining distance-weighted keeping transformation to obtain a multi-channel weighted isometric function; using recursive filtering to realize one-dimensional nonlocal domain transformation filtering of linear time; conducting separated one-dimensional nonlocal domain transformation filtering to the two-dimensional color image. The nonlocal domain transformation method can effectively involve nonlocal information into an image filtering algorithm. Under the premise that a small amount of computation is added, a high-quality filtering image is effectively obtained, and meanwhile the two aspects of requirements that the edge information in the image is not fuzzy are taken into account. A single color (or gray) image is adopted as an input source, the algorithm complexity is lower, the filtering effect is good, and a variety of computer vision application requirements can be met. The method is especially suitable for image detail enhancement, picture stylizing, image colorizing and pencil drawing conversion.

The present invention provides an acoustic vector circular array mode-domain robust direction estimation method. The method includes the following steps that: the received signals P(t), Vx (t) and Vy (t) of an acoustic vector circular array acoustic pressure channel, an acoustic vector circular array vibration velocity x channel and an acoustic vector circular array vibration velocity y channel are obtained; the received signals Pe (t), Vex (t) and Vey(t) of a phase mode-domain acoustic pressure channel, a phase mode-domain vibration velocity x channel and a phase mode-domain vibration velocity y channel are obtained; Vex (t) and Vey(t) are rotated electronically, so that a combined vibration velocity Vec (t) can be obtained, a covariance matrix Repv is obtained based on Pe (t) and Vec (t), an unitary matrix Q is introduced to transform the matrix Repv, so that a real-value covariance matrix Rpv of mode-domain acoustic pressure and vibration velocity combined processing can be obtained; a steering vector a<~>r(Phi<~>) which has been subjected to mode-domain transformation and real-value processing is constrained, second-order cone programming is adopted to carry out solving, so that an optimal weight vector Omegarob can be obtained; and an output space spectrogram can be obtained based on the steering vector Omegarob which has been subjected to mode-domain transformation and unitary matrix real-value transformation, and a target direction can be obtained based on a spectrum peak position. With the method of the invention adopted, the problems of performance degradation and high signal-to-noise ratio processing threshold of a minimum variance distortionless response algorithm under a condition that related sound sources are difficult to distinguish and mismatching occurs can be solved. The method has the advantages of high resolution, high robustness, small amount of calculation, strong background noise suppression ability and the like.

The invention discloses a cross-domain target detection method based on a regional full convolutional network and self-adaptation, and belongs to the technical field of computer vision. According to the method, a deep learning target detection technology is used, and the cross-domain robustness of target detection is improved by using an adaptive method in allusion to the problem of different distribution of data of a training domain and a test domain in target detection. The method comprises steps of firstly, constructing a regional full convolutional network model based on deep learning; designing two corresponding domain classifiers on the image level and the target level as adaptive components to reduce the difference of domain transformation, and adding consistency regularization to the domain classifiers; training the network in an end-to-end mode; and finally, removing adaptive components, and applying the network to a target detection task. By adopting the cross-domain target detection method designed by the invention, the average precision of target detection in various domain transformation scenes can be effectively improved.

The invention discloses an aerial photographing visible light image processing method comprising the steps that a visible light image and an infrared image are respectively acquired by simultaneously using a visible light sensor and an infrared sensor; YUV color domain transformation is performed on the visible light image so that a YUV color space image is generated, and the YUV color space image is decomposed into a brightness domain Y component image, a color domain U component image and a color domain V component image; Gaussian low-pass filtering and noise elimination are performed on the brightness domain Y component image obtained through decomposition so as to obtain a brightness domain image; sharpening processing is performed on the infrared image so as to obtain a final sharpening image; the brightness domain image and the final sharpening image are fused so as to generate a final brightness domain Y component image; and hue saturation adjustment is performed on the color domain U component image and the color domain V component image so as to obtain a final color domain U image and a final color domain V image. The technical problems of image edge blurring and detail failure caused by using visible light image noise elimination and enhancement in the present method can be solved by the aerial photographing visible light image processing method and system.

The invention includes an Ethernet transceiver. The Ethernet transceiver includes a plurality of digital signal streams, at least one digital signal stream is coupled to another of the digital signal streams. A transform block transforms a plurality of the digital signal streams from an original domain into a lower complexity processing domain. A processor for joint processes the transformed digital signal streams, each joint processed digital signal stream is influenced by other digital signal streams. An inverse transform block inverse transforms the joint processed signal streams back to the original domain. Another embodiment of the invention includes a method of joint processing a plurality of digital signal streams. A first act of the method includes transforming a plurality of the digital signal streams from an original domain into a lower complexity processing domain. A second act of the method includes joint processing of the transformed digital signal streams, each joint processed digital signal stream being influenced by characteristics of other digital signal streams. A third act includes inverse transforming the joint processed signal streams back to the original domain.

The invention discloses a data transmission system based on an FPGA and a DSP. The data transmission system comprises an FPGA module, a DSP module and an external memory. The FPGA module comprises a memory read-write control unit, a data processing unit, an asynchronous FIFO unit, a synchronous FIFO unit and a data read-write detection unit. The memory read-write control unit reads the data of predetermined units from the external memory. The data processing unit is used for processing the data of all the units. The asynchronous FIFO unit is used for conducting clock domain transformation on the data of the predetermined units. The synchronous FIFO unit caches the data of the predetermined units, and an interrupt signal is sent to the DSP module when data arrive at the predetermined units. The DSP module reads the data from the synchronous FIFO unit according to the interrupt signal. When the data read-write detection unit detects that the DSP module reads the data of one unit every time, a feedback signal is sent to the memory read-write control unit, so that the memory read-write control unit reads the data of the next unit from the external memory. The data transmission system based on the FPGA and the DSP can guarantee the data transmission continuity between the FPGA and the DSP under an EMIF transmission mode.

The invention provides a single image defogging method based on improved non-local prior and a medium, and belongs to the technical field of computer vision and the technical field of image processing. The single image defogging method comprises the following steps: firstly, carrying out atmospheric light estimation by using an improved dark channel prior method, and then processing the whole image by using non-local prior to estimate the initial transmissivity, wherein global information is considered in non-local prior, and the image does not need to be segmented into different blocks, so that the phenomenon of wheel halo can be avoided; secondly, refining the scene transmissivity by adopting context regularization based on a weighted L1 norm to obtain refined scene transmissivity; and finally, correcting the transmissivity by using domain transformation filtering. Evaluation indexes such as peak signal-to-noise ratio, visible edge number, average gradient, saturated pixel point number and the like are improved.

The invention discloses a hyperspectral image vegetation classification method for spatial feature adaptive optimization. The method comprises the steps that first, normalization processing is performed on a hyperspectral image, PCA dimension reduction is performed on the hyperspectral image obtained after normalization processing, morphological filtering is performed on the hyperspectral image obtained after PCA dimension reduction, and a spatial information set is acquired; second, domain transformation standard convolution filtering is performed on the hyperspectral image obtained after normalization processing for spatial information extraction, and spatial information linear superposition fusion is performed on the acquired spatial information set; and finally the spatial informationset obtained after fusion is classified. Through the method, spatial texture information and relevancy information are effectively utilized, filtering parameters of a best classified structural type is further optimized, a good filtering effect is achieved, and classification precision of the hyperspectral image is effectively improved.

The invention discloses an image editing and communication method. The method includes the following steps that (1), an original image to be processed is input, a depth image of the original image is obtained; (2), the communication color, expected by a user, of the original image is received; (3), domain transformation is respectively performed on the original image and the depth image, and the domain transformation result of the original image and the domain transformation result of the depth image are combined to obtain a mixed domain transformation result; (4), the domain transformation result in the step (3) is used for filtering color information input by the user in the step (2) to obtain chroma reference values of all edited pixel points in the images; (5), according to the obtained chroma reference values of all the edited pixel points in the step (4), chromatic values of all the pixel points are determined, the chromatic values and gray values of the original image are combined, and the result image after editing and communication are performed is determined. The image editing and communication method is good in editing and communication effect, no color communication errors or disorder exists, and color communication on the edges is correct.

The invention provides a spatial information adaptive fusion-based hyperspectral image classification method. According to the method, spectral information is extracted as spatial information by usingtwo filters and is classified, so that spatial texture information and correlation information are effectively utilized; through domain transformation standard convolution filtering, a certain spatial texture information can be extracted, and relatively good spatial correlation is kept, so that the deficiency that only the spatial texture information can be extracted through bilateral filtering is made up for; and after adaptive fusion of the spatial texture information extracted through the bilateral filtering and the domain transformation standard convolution filtering, optimal classification performance is obtained through SVM classification, and a non-uniform phenomenon is effectively eliminated, so that the method is especially suitable for hyperspectrum with relatively numerous ground objects and complex distribution.

The invention relates to the hydroacoustic engineering technology field, particularly relates to a circular array modal domain orientation estimation method based on space sparse constraint and aims to solve problems that multi-shot sampling data is required to construct a covariance matrix, inversion or characteristic value decomposition is required for the covariance matrix and algorithm execution efficiency is relatively low existing in a modal domain orientation estimation method in the prior art. The method comprises steps that a uniform circular array sound pressure signal is acquired; aphase modal domain transformation matrix is constructed; a sound pressure signal received by a uniform circular array is transformed into a phase modal domain signal through a phase modal domain transformation matrix; the 360-DEG full space orientation is discretized, and a space domain sparse transformation base is further constructed; time domain sampling of a uniform circular array reception signal is carried out to construct multi-shot sampling data in a phase modal domain, and optimization and solution of multi-shot sparse signals are carried out through utilizing the l1 norm; signal energy in different orientation angles is calculated, a spatial spectrum is drawn, and the target orientation is acquired through the maximum peak position. The method is advantaged in that the method issuitable for signal estimation of the hydroacoustic engineering.

The invention provides a depth image optimizing method based on natural scene statistics. The method includes providing a flat image and an initial depth image of the flat image, extracting brightness information of the flat image, conducting domain transformation filtering on the brightness information, conducting wavelet decomposition on the filtered brightness information and the initial depth image, building a Gaussian model for a brightness wavelet pyramid, estimating a shape parameter and an extension parameter, building an energy function of a depth wavelet pyramid, the shape parameter and the extension parameter, minimizing the energy function to obtain the optimized depth wavelet pyramid, and conducting wavelet synthesis on the optimized depth wavelet pyramid to obtain the optimized depth image. By means of the optimizing method, the depth image can be optimized quickly, and depth information accuracy of the depth image is improved, so that the stereo video quality can be improved after the depth image is converted into stereo video, and user viewing experience is improved.

The invention provides an improved frequency dispersion ultrasonic guided wave signal domain transformation method, and belongs to the field of ultrasonic guided wave structure health monitoring. Themethod includes the following steps: (1) obtaining the frequency dispersion wave number curve and the frequency non-dispersion wave number curve of an ultrasonic guided wave signal mode; (2) calculating the ultrasonic guided wave distance domain excitation waveform with a reduced space width; (3) calculating an ultrasonic guided wave distance domain impulse response signal; and (4) calculating a resolution enhanced non-dispersive ultrasonic guided wave distance domain signal. Compared with traditional time-distance domain signal transformation methods, the method provided by the invention recompresses a frequency dispersion-expanded wave packet in the original ultrasonic guided wave time domain signal through dispersion compensation and changes the distance scale of the excitation waveformin order to further reduce the distance domain width of each wave packet in the ultrasonic guided wave signal, so the spatial resolution of the original frequency dispersion ultrasonic guided wave signal is effectively enhanced.

The invention belongs to the field of processing of gravity and magnetic data, in particular discloses a method for separating a gravity and magnetic field in a DCT domain, which comprises the following steps: carrying out gridding treatment on two-dimensional discrete gravity and magnetic data obtained by field measurement, and forming regular grid data; converting the grid data into the DCT domain from a space domain, and obtaining a frequency spectrum of original data; setting n different threshold values En, and counting an area An surrounded by all sets, the energy spectrum values of which are larger than En; drawing a logEn-logAn bilogarithmic graph in a rectangular Cartesian coordinate system, using a least square method to fit a plurality of straight-line segments with different slopes, and determining an energy spectrum boundary value of a filter according to the energy spectrum value corresponding to the intersection point of the straight-line segments; carrying out filteringtreatment on the gravity and magnetic field according to the determined energy spectrum boundary value; and carrying out the inverse discrete cosine transform to the filtering result and converting the filtering result into the space domain from the DCT domain to acquire the separating result of the original data. The method of the invention has stronger decorrelation ability, fully considers thespatial self-similarity or multi-fractal characteristics of the gravity and magnetic field, and can effectively separate the gravity and magnetic field from a local field.

Image data to be compressed is first converted from the RGB domain into a gamma-powered YUV domain. A wavelet transform then separates image data into high- and low-detail sectors, incorporating a dynamic scaling method, allowing for optimal resolution. The output data from the wavelet transform is then quantized according to an entropy-prediction algorithm that tightly controls the final size of the processed image. An adaptive Golomb engine compresses the data using an adaptive form of Golomb encoding in which mean values are variable across the data. Using variable mean values reduces the deleterious effects found in conventional Golomb encoding in which localized regions of similar data are inefficiently coded if their bit values are uncommon in the data as a whole. Inverse functions are applied to uncompress the image, and a fractal dithering engine can additionally be applied to display an image on a display of lower color depth.