130results about How to "Suppress speckle noise" patented technology

The invention relates to a fusion method of SAR (Synthetic Aperture Radar) images and visible light images on the basis of NSCT (Non Subsampled Contourlet Transform). The fusion method is characterized by comprising the following steps of: firstly, carrying out NSCT decomposition on the SAR images and the visible light images respectively; then adopting different fusion rules to carry out fusion treatment on NSCT low-frequency and high-frequency subband coefficients, wherein according to the decomposition coefficient characteristics of noise and signals in an NSCT domain, carrying out hard-threshold denoising on the NSCT high-frequency subband coefficient of the SAR images under the maximum decomposition scale, then respectively adopting different fusion rules to carry out fusion processing on the NSCT high-frequency subband coefficient under the maximum decomposition scale and other decomposition scales by adopting the coefficients with threshold processing as the basis; and finally,carrying out NSCT reverse transformation on the fused NSCT coefficients and obtaining fused images. The fusion method takes denoising as the basis of the fusion rule design, considers noise suppression while fusion treatment is carried out, is simple and easy to operate, can be used for obtaining a good fusion effect and is especially more applicable to the SAR images and the visible light imageswith serious spot and noise pollution.

The invention provides a method for improving the quality of ultrasonic images, which is used for the optimization of display data of ultrasonic scan images in an ultrasonic imaging system. The method comprises the following step of decomposing ultrasonic image date into a plurality of layers by using a Gaussian-Laplacian pyramid decomposition method, carrying out anisotropic diffusion, filtering processing to Gaussian layer data of the decomposition result, carrying out reverse reconstruction to the processed ultrasonic image data, and improving the quality of the ultrasonic images through a plurality of times of iterative processing, wherein a anisotropic diffusion and filtering algorithm is used to calculate diffusion factors in different directions according to the local structural information of the ultrasonic images so as to filter the image data. As a result of the method, information in organizational-structure regions is enhanced and the noises in non-organizational-structure regions are suppressed effectively.

The invention provides a medical ultrasonic image denoising method based on wavelet transform and a trilateral filter. The medical ultrasonic image denoising method comprises the following steps that step 1) a medical ultrasonic image model is established; and step 2) wavelet decomposition is performed on an image obtained in the step 1 after logarithmic transform so that four frequency domains (LL1, LH1, HL1 and HH1) are obtained. Wavelet decomposition is continuously performed on the low frequency domain LL1 so that four frequency domains (LL2, LH2, HL2 and HH2) are obtained again; then the step is repeated until the maximum number J of layers of decomposition; step 3) threshold method contraction processing is performed on the wavelet coefficient of the high frequency part (LHj, HLj and HHj, j=1,2,...,J) of each layer; step 4) filtering processing is performed on the wavelet coefficient of the low frequency part (LLJ) of the last layer by utilizing the trilateral filter; and step 5) inverse wavelet transform processing is performed so that a denoised medical ultrasonic image is obtained.

The invention discloses a method for detecting oil spill at sea based on a full-polarized synthetic aperture radar (SAR) image. The method includes the following steps: pre-treating polarized SAR data which require analyzing to obtain a full-polarized SAR covariance matrix; conducting refining polarized Lee filtering on the covariance matrix; based on the filtered covariance matrix or through a Stokes vector calculated by the covariance matrix, extracting 6 polarized features to constitute a feature combination; inputting training sample features with ground verification label information to a maximum likelihood (ML) classifier, training the classifier and optimizing parameters of the classifier; taking polarized features as input, utilizing the ML classifier to conduct detection and classification on an oil membrane; processing a classification result based on morphology, utilizing verification information of actual measurement to evaluate classification precision. According to the invention, the method can increase performances of oil spill at sea detection and classification methods and promote the application of the full-polarized SAR in actual engineering problems such as monitoring of oil spill at sea.

The invention discloses a non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method and belongs to the technical field of image processing. The problems that a traditional triplet Markov field (TMF) method which is used in SAR image segmentation is poor in regional consistency and disorder in edge are solved. The method comprises steps of (1) inputting an image to be segmented; (2) initializing all pixel class marks by using fuzzy C-means (FCM) clustering; (3) initializing all pixel scene categories by using k-means and conducting iteration for scene categories by using non-local redundant information; (4) calculating potential energy of the image; (5) constructing triple Markov random field joint distribution, conducting function sampling for the distribution by using a Gibbs sampler and obtaining the posterior probability; (6) calculating the edge posterior probability and updating all pixel class marks gradually; and (7) determining whether the change rate of all pixel class marks is larger than the threshold, repeating step (4), step (5) and step (6) if the change rate of all pixel class marks is larger than the threshold, and inputting segmentation results if the change rate of all pixel class marks is not larger than the threshold. The method has the advantages of being quick in convergence velocity, good in segmentation result regional consistency, capable of retaining complete information and applied to SAR image target identification.

The present invention relates to a method and apparatus for speckle noise reduction in laser scanning projection display. In particular, a MEMS device with a vibrating membrane through which light rays are refracted with temporally varying angles is provided for reducing the effect of speckling.

The utility model discloses a silencing method of synthetic aperture radar image self adaptive speckle, providing a standard utilizing a global statistic heterogeneity entropy He to judge regional homogeneous or heterogeneous. Because the heterogeneity entropy He can utilize the global probability and statistics distribution of the image, the changes of edge detail and texture information can be measured accurately. And then the local homogeneous or heterogeneous of the image is judged more accurately and self adaptively utilizes different noise reduction measures. The method can inhibit the speckle noises of synthetic aperture radar image without sacrificing the spatial resolution, and reserve the role of the fringe goals texture edge details.

The invention discloses a method for de-noising a medical ultrasonic image based on wavelet transformation and a guide filter, and the method comprises the following steps of: step 1) creating a medical ultrasonic image model; step 2) performing wavelet decomposition to the logarithm transformation image in the step 1) to obtain four frequency domains (LL1, LH1, HL1 and HH1); performing wavelet decomposition to LL1 in the low frequency domain so as to obtain four frequency domains (LL2, LH2, HL2 and HH2); repeating the step until to decompose the maximum layer J; step 3) performing filter processing to a wavelet coefficient in the final-layer low-frequency part (LLJ) by the guide filter; step 4) performing threshold valve method contraction treatment to the wavelet coefficient in each-layer high-frequency part (LHj, HLj and HHj, j=1, 2, ......, J); step 5) performing wavelet inverse transformation treatment to gain the de-noised medical ultrasonic image; performing exponential transformation to the medical ultrasonic image obtained in the step 5) to obtain de-noised ultrasonic envelope signals.

The invention discloses an adaptive denoising method for an ultrasonic image. The adaptive denoising method comprises the following steps: (1) obtaining the ultrasonic image; (2) performing Gaussian-Laplacian pyramid decomposition on the ultrasonic image and obtaining Gaussian layers and Laplacian layers at different scales; (3) calculating a structure tensor and a diffusion tensor of the Gaussian layer at each scale and performing anisotropic diffusion filtering treatment on the Gaussian layer at the scale; (4) according to a characteristic value of the structure tensor of the Gaussian layer, designing a grey mapping curve, and performing grey mapping on the Gaussian layer at the scale according to the grey mapping curve; (5) repeating the steps (3) and (4) for multiple times and performing the same treatment on the Gaussian layer and the Laplacian layer at each scale; (6) performing reverse reconstruction on the treated Gaussian layer and Laplacian layer and obtaining a denoised ultrasonic image; (7) outputting the denoised ultrasonic image. The adaptive denoising method can inhibit edge enhancement and spots of the image and is simple in algorithm and strong in adaptability.

The present invention discloses an SAR remote sensing image change detection method based on wavelet transform and an SVM. The method is characterized in that firstly, the logarithmic ratio image and a mean value ratio image of two time phase remote images are carried out, then stationary wavelet transform is used to fuse the logarithmic ratio image and the mean value ratio image to form a difference image, and finally the bipartition (a change class and a non-change class) of the difference image is carried out through a progressive direct push SVM to obtain a final change detection result. The method can be used to solve the problems of poor detection precision and poor anti-noise performance of multi-time-phase SAR remote sensing image change detection, the speckle noise in the SAR remote sensing image is effectively inhibited, and the problems of low precision and narrow application range of single type of difference image detection are solved.

The invention provides a medical ultrasonic image denoising method based on a statistical model. The medical ultrasonic image denoising method comprises the following steps that step 1) a medical ultrasonic image model is established; step 2) wavelet decomposition is performed on an image after logarithmic transformation obtained in the first step so that four frequency domains (LL1, LH1, HL1 and HH1) are obtained; wavelet decomposition is continuously performed on the low frequency domain LL1 and then four frequency domains (LL2, LH2, HL2 and HH2) are obtained; and then the step is repeated until the maximum number J of layers of decomposition; step 3) threshold method contraction processing is performed on the wavelet coefficient of the high frequency part (LHj, HLj and HHj, j=1,2,...,J) of each layer; step 4) filtering processing is performed on the wavelet coefficient in the low frequency part (LLJ) of the last layer by utilizing a guidance filter; and step 5) wavelet inverse transformation processing is performed so that a denoised medical ultrasonic image is obtained.

The invention discloses a saliency fusion-based SAR image change detection method, and aims at solving the problem that the SAR image change detection in the conventional method is easy to be influenced by speckle noise and is low in detection precision. The method comprises the following specific steps: (1) inputting an SAR image; (2) carrying out filtering; (3) calculating the logarithmic ratio of a pixel; (4) constructing a global saliency map; (5) calculating contrast ratio saliency values of different scales; (6) constructing a local saliency map; (7) fusing the local saliency map and the global saliency map; (8) carrying out fuzzy clustering; (9) outputting a change detection result. The method has the advantages of being good in noise influence robustness and high in detection precision for SAR image change detection.

A projection display apparatus of the present invention can include a light source; an optical modulator, configured to repeatedly output a modulation beam N times in a predetermined time range, the modulation beam being generated by identically modulating a bema of light emitted from the light source and N being a nature number and equal to or greater than 2; a mirror, configured to reflect the modulation beam, which is repeatedly outputted N times, to scan the modulation beam to an identical point of a screen; and a polarization rotating unit, configured to rotate a polarization direction of the modulation beam for each time the modulation beam is scanned. With the present invention, the speckle noises on a video scanned to a screen can be suppressed.

The invention relates to a real-time phase shifting method for high signal-to-noise ratio speckle interferometry. The method utilizes a piezoelectric phase shifter to generate multistep phase shifting, a video camera simultaneously acquires a plurality of phase shifting speckle images, a phase shifting image of a speckle interference fringe is calculated through a special image acquisition serial design and a quick algorithm, comprises the phase information which represents the object deformation at the current moment, and can be demodulated through the phase. Through the special image acquisition serial design and the quick algorithm, the camera with the image acquisition speed of 20 frame/second is taken as an example, the method can complete from original image acquisition to the calculation and display of the phase shifting image within 100-150 millisecond, so the phase shifting calculation speed is greatly increased; and compared with the traditional speckle interferometry phase shifting method, the method can better suppress speckle noise, obviously improve the image contrast, and realize real-time phase extraction.

The invention discloses a noise reduction processing method of an ultrasound medical image. The noise reduction processing method of the ultrasound medical image is characterized by comprising the following steps of calculating an edge intensity image E of a gray-scale ultrasound image I; calculating a mask image P in a smooth organization structure area of the gray-scale ultrasound image I; calculating an edge intensity threshold curve Kcurve of the gray-scale ultrasound image I along a depth direction of an ultrasonic probe scanning line; extracting a corresponding edge intensity threshold from the edge intensity threshold curve Kcurve and utilizing a diffusion coefficient function g to obtain a diffusion coefficient for any ith row image Ei of the edge intensity image E; and utilizing a diffusion equation (formula as follows, wherein all portions of the formula respectively stand for an image the gray-scale ultrasound image I evolving to time t to be formed into, an evolution time step length, a divergence operator and a gradient of the gray-scale ultrasound image I) to perform filtering processing on the image according to the generated diffusion coefficient. The noise reduction processing method of the ultrasound medical image has the advantages of enabling the ultrasound medical image to be capable of utilizing different edge intensity thresholds along the depth direction of probe scanning and achieving the effect of speckle noise suppression optimization.

The invention discloses a method for denoising a medical ultrasonic image based on a normal inverse Gaussian model, comprising the following steps: 1) using a noise imaging system to collect the envelope signal of the noise image, and establishing a medical ultrasonic image model; Step 1) The medical ultrasound image model obtained after the logarithmic transformation utilizes a pyramid filter bank to perform multi-scale and multi-directional decomposition, 3) For the two-dimensional discrete shearlet of the high-frequency part in each sub-band image obtained in the step 2) The transformation coefficients are subjected to threshold method adaptive shrinkage processing; 4) use the trilateral filter to filter the shearlet coefficients in the low-frequency part of step 2); 5) perform shearlet inverse transformation on all coefficients after step 3) and step 4) processing After processing, the medical ultrasound image after denoising is obtained. The invention can well suppress the speckle noise, and at the same time can better help doctors to analyze the condition.

The invention discloses a real-time compression and reconstruction method of a Video-SAR (Synthetic Aperture Radar) image. A Robust-PCA (Principal Component Analysis) is used for effectively inhibiting coherent speckle noise in the Video-SAR image, and detail texture characteristics are favorably retained. Through the training and learning of an optimal Wavelet packet basis dictionary, good sparse representation on the Video-SAR image is realized so as to reduce a measurement amount, improve reconstruction precision and store the parse matrixes of different types of scenes, and the parse matrixes are directly called during dilution representation to reduce an operation amount. The frame difference of the key frame and the non-key frame of the Video-SAR image is subjected to low-complexity compression coding at different sampling rates, and distributed compression perception reconstruction can be adopted to meet real-time transmission requirements. A Canny operator is used for carrying out edge detection to realize a purpose that the non-key frame is subjected to Bregman iteration fine decoding, and the high-resolution SAR image with rich details can be reconstructed.

The invention discloses a polarization SAR image filtering method for window self-adaptation. An image is filtered through a window template which is self-adaptive in size and shape, namely, a large square window is used for performing filtering in a heterogeneous area, a small non-square window is used for performing filtering in the homogenous area, point information, line information, edge information and other detailed information are kept while the noise is restrained, and the coherent speckle noise restraining effect is improved to the maximum degree.