47 results about "Stationary wavelet transform" patented technology

The invention discloses a natural image denoising method based on the regional division, which mainly solves the problem that patches exist in an image after an image is donoised through the existing three-dimensional block matching denoising algorithm. The method comprises the following steps: firstly, performing two-dimensional stationary wavelet transform on an input image to be denoised, and performing inverse transform on a high frequency coefficient subjected to zero setting, so as to obtain a reconstructed image; secondly, exacting the structural information of the reconstructed image, so as to obtain an image structure sketch; thirdly, dividing the noisy original image into a structure region, a smooth region and a non-smooth region through statistic features of an image block and the image structure sketch; fourthly, performing denoising on the smooth region with an improved non-local mean method, performing denoising on the non-smooth region with a BM3D (Block matching 3D) method, and performing denoising on the structure region with a directional feature-based BM3D method; and fifthly, combining estimated results of the structure region, the smooth region and the non-smooth region, so as to obtain the final denoised image. The method can be used for preprocessing natural images.

The invention discloses a fetal electrocardiogram signal extracting method based on wavelet threshold denoising. The method comprises the following steps of: carrying out stationary wavelet transform processing on a maternal abdomen signal, and decomposing to obtain each layer of wavelet coefficient; independently processing each layer of wavelet detail coefficient, removing fetal electrocardiogram wavelet coefficients, reconstructing remained maternal electrocardiogram wavelet coefficients, and obtaining maternal electrocardiogram in the abdomen signal; removing the reconstructed maternal electrocardiogram from the abdomen signal to obtain a fetal electrocardiogram signal; and denoising the extracted fetal electrocardiogram by utilizing a wavelet correlation denoising algorithm to obtain a clear fetal electrocardiogram signal. Due to the method, the accurate extraction of the fetal electrocardiogram signal can be realized. The method is based on a single channel, the computation of wavelet transform modulus maxima is avoided, and better instantaneity is realized.

The invention discloses a foggy day image salient target detection method, which comprises the following steps of: step 1, performing color space conversion on a foggy day image in a frequency domainto calculate the saliency of the foggy day image, and solving a frequency domain saliency map; step 2, performing super-pixel segmentation on the foggy day image in a spatial domain, calculating the significance of each super-pixel block, and solving a spatial domain saliency map; step 3, fusing the saliency map of the image in the frequency domain and the saliency map of the image in the space domain into a saliency map through discrete stationary wavelet transform; step 4, obtaining a contour map of the foggy day image through the target contour detection model; and step 5, adding the saliency map fused based on the frequency domain and the spatial domain to the contour map to obtain a final saliency map. According to the method, a traditional machine method and a deep learning method are combined, the robustness of traditional significant target detection is improved, and a significant target in a foggy day scene can be efficiently and accurately detected; meanwhile, for some imageswith complex backgrounds, a significance target can be well extracted.

The invention relates to an optical image denoising method based on serial connection of curvelet transform and wavelet transform. The method comprises the steps of 1, selecting a wave band, with noise, of optical data as an image to be processed; 2, carrying out forward Curvelet transform on the image to be processed to obtain multi-scale and multi-direction Curvelet transform coefficients; step 3, with noisy data in the Curvelet transform coefficients to be removed through wavelet transform, carrying out wavelet transform denoising and then carrying out reverse wavelet transform to obtain denoised Curvelet coefficients; 4, carrying out reverse Curvelet transform on the denoised Curvelet coefficients to obtain a denoised reconstructed image. Compared with single curvelet transform or single wavelet transform, the image denoised through the method has a higher peak signal-to-noise ratio and a better visual effect, the stripe noise of the optical image is removed, detail information of the image is well reserved, and the quality of the image is improved.

The invention discloses an SAR and optical image fusion method for landslide detection. The method provided by the invention comprises the following steps: step 1, preprocessing an SAR and an opticalimage; step 2, performing HIS conversion on the optical image to obtain three components I, H and S; step 3, performing stationary wavelet transform and high-frequency component energy maximization fusion on I components of the SAR image and the optical image; step 4, performing saliency detection on the low-frequency and high-frequency components of the SAR image and the gray scale information ofthe image, establishing an SAR salient target detection area guidance function, and partitioning the SAR image; step 5, establishing a salient region fusion rule, and realizing image fusion accordingto a regional fusion strategy; and step 6, identifying and extracting landslide disaster information based on the fused image. The method has good adaptability to SAR and optical image fusion for landslide detection, adopts related processing measures in structure maintenance, noise removal and spectrum reservation, and obtains an excellent effect.

The invention discloses a high-voltage direct-current transmission line calculation model based on distributed resistance parameters. The method comprises the steps of (1) acquiring a fault voltage signal and a current signal; (2) carrying out decoupling to obtain aerial mode components; (3) calculating voltage and current aerial mode components of a direct-current transmission line along the lineby utilizing a distributed parameter line model based on distributed resistance; (4) calculating a reverse traveling wave current of the direct-current transmission line along the line; and (5) calculating a modulus maximum value of the reverse traveling wave current by adopting stationary wavelet transform. According to the method, after voltage and current values of measuring points at the twoends of the line are extracted, voltage and current aerial mode component values of the direct-current transmission line along the line are calculated by utilizing the line model based on the distributed resistance; then according to the characteristics that a first reverse traveling wave is not reflected yet, the influence of frequency change of a reflection coefficient is avoided and fault features are obvious, a reverse traveling wave current value of the direct-current transmission line along the line is calculated; and finally, the modulus maximum value of the reverse traveling wave current is calculated through the stationary wavelet transform, so that the accuracy of the transmission line calculation model is improved.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on multi-scale feature fusion, comprising the steps of: firstly, extracting the textural features of an image by utilizing fast discrete curvelet transform (FDCT), extracting the statistic features of the image by utilizing stationary wavelet transform (SWT), then, fusing the two type of multi-scale featuresinto high-dimensional feature vectors, and finally, segmenting by adopting a fuzzy C-mean clustering method. At the same time of eliminating broken pieces in a homogenizing area, the boundary is moreprecise and smoother. The method is favorable for effectively preventing the extraction of textural information from the interference of speckle noises. By fusing SWT coefficient statistical features, the textural information and the grey statistical information of the SAR image are effectively utilized and a favorable segmentation effect can be achieved.

The invention discloses a stationary wavelet transform (SWT) improved particle filter-based synthetic aperture radar (SAR) image despeckling method, which mainly solves the problems of low statistical modeling accuracy, fuzzy image edge and texture after despeckling and the like in a conventional discrete wavelet transform (DWT) domain basic particle filter method. The realization process of the method comprises the following steps of: (1) transforming an airspace image to be despeckled to obtain a stationary wavelet domain and extracting SWT domain image groups with different transform scales in horizontal, vertical and diagonal directions; (2) performing despeckling processing on the SWT domain image groups by using an improved particle filter despeckling method; and (3) transforming the despeckled stationary wavelet domain image groups to airspace by using stationary wavelet inverse transform, wherein the airspace image is a finally despeckled result. Compared with the conventionalDWT domain basic particle filter method, the SWT improved particle filter-based SAR image despeckling method has a stable result, an obvious despeckling effect and remarkable image texture information, single point target and boundary maintaining effects, and can be used for target detection and target identification.

The invention relates to a method for detecting P and T characteristic waves of an electrocardiosignal. The method comprises the following steps of: a, acquiring the electrocardiosignal of a human body to perform pre-processing; b, carrying out smoothing processing on the signals under the scale of the to-be-detected sections of the P and T characteristic waves through stationary wavelet transform; c, carrying out electrocardio data P and T characteristic wave peak value point detection by utilizing a maximum and minimum value principle; d, correcting the positions of P and T characteristic wave peak points of the electrocardio data according to a time shift correction principle; and e, evaluating a detection result by calculating three indexes of the error rate e<r>, the sensitivity S<e>and the correct prediction degree P of P and T characteristic wave detection. The electrocardiosignal is processed and smoothed by the method in the invention; therefore, the noise is effectively filtered; furthermore, low-frequency characteristic waves of the electrocardiosignal are recovered; and effective information of the electrocardiosignal is reserved.

The invention discloses an identification method of hemispherical and conical models based on discrete stationary wavelet transform. The method includes: firstly, obtaining point cloud data of a measured curved surface by employing a three-dimensional scanner; then obtaining an elevation image of the measured curved surface and elevation sequences of pixels of various rows and columns in the image by employing a gridding method; processing the obtained elevation sequences by employing discrete stationary wavelet transform to obtain corresponding wavelet detail coefficient sequences; further deducing an ideal mathematical expression of the measured curved surface according to corresponding relations between the elevation sequences and the wavelet detail coefficient sequences; calculating ideal elevation sequences of the measured curved surface and corresponding wavelet detail coefficients according to the estimated ideal mathematical expression; and finally analyzing wavelet detail coefficient sequences corresponding to the elevation sequences of actual measurement data and the wavelet detail coefficient sequences corresponding to the ideal mathematical expression of the measured curved surface to realize identification of a measured model.

The invention provides a wavelet dual-threshold denoising method based on an interlayer correlation coefficient, which comprises the following steps of: processing a noisy signal by adopting stationary wavelet transform (SWT) to obtain an output coefficient of a low-frequency part and an output coefficient of a high-frequency part, and according to each group of lower threshold regulation factors represented by each firefly, processing the output coefficient of the high-frequency part by adopting a wavelet dual-threshold function based on an interlayer correlation coefficient, then reconstructing the processed output coefficient of the high-frequency part and the processed output coefficient of the low-frequency part to obtain a denoised signal, and finally taking the signal-to-noise ratio of the denoised signal as the brightness of fireflies. The higher the signal-to-noise ratio is, the higher the brightness of the fireflies is, the better the denoising effect of the lower threshold adjustment factor represented by each firefly is, and the optimal denoising signal is obtained by using the firefly algorithm. According to the method, a layered threshold value and a semi-soft threshold value method are combined, the firefly algorithm is added, and given threshold values are optimized through a signal-to-noise ratio (SNR) index; and the threshold value precision is improved.

An Electrocardiography (ECG) noise-filtering device is provided in the invention. The ECG device includes a filter and a calculation circuit. The filter receives a first ECG signal and performs a Savitzky-Golay algorithm to generate a second ECG signal. The calculation circuit is coupled to the filter to receive the second ECG signal and processes the second ECG signal according to a Stationary Wavelet Transform (SWT) algorithm to generate a noise signal, and subtracts the noise signal from the second ECG signal to filter the noise signal in the first ECG signal.