48 results about "Exponential transformation" patented technology

The invention discloses a PCNN-based method for de-noising a wavelet domain ultrasonic medical image, which comprises the following steps: firstly, performing logarithmic transformation and wavelet transformation on a noise image and the corresponding pretreatment on a wavelet coefficient; secondly, processing the wavelet coefficient by using a PCNN method and performing the corresponding post-treatment on the wavelet coefficient; and finally, performing the inverse wavelet transformation and exponential transformation on the wavelet coefficient subjected to the post-treatment to obtain a de-noised image. Compared with wavelet de-noise, the method makes the edge of the de-noised image clearer, well retains image details and improves a signal to mean square error ratio; and compared with PCNN, the method overcomes the drawback that during the removal of speckle noises, the PCNN has difficulty in determining model parameters and step length and realizes a higher signal to the mean square error ratio after image de-noise.

A method for determining an individual's intensity of focused attention, comprising the steps of: obtaining a representative frontal lobe brainwave signal from at least one first sensor in an electrically connective relation to the individual's frontal lobe; obtaining a representative reference signal from at least one second sensor in an electrically connective relation to a more electrically-neutral location; subtracting the representative reference signal from the representative frontal lobe brainwave signal to produce a difference frontal lobe brainwave signal, and processing the difference frontal lobe brainwave signal to produce an Attention Indicator signal indicative of the individual's intensity of focused attention, where the Attention Indicator signal is inversely proportional to any mathematical transformation of an amplitude measure of the difference frontal lobe brainwave signal; inputting the Attention Indicator signal to a device; and, repeating these steps, as desired. The mathematical transformation can relate to amplitude, power or any linear, logarithmic or exponential transformation thereof.

The invention relates to the technical field of website data statistic analysis and particularly discloses a website page view prediction method based on historical tendency weights. The method comprises the steps of data preprocessing and prediction result processing, wherein in the data preprocessing step, the logarithm of page views is taken, the variance of all time points in the historical tendency is calculated, the regression coefficient of the ith historical tendency to the current tendency is calculated, the variance of correlation coefficients of a current value estimated according to the ith historical tendency is calculated, deviation is estimated through an optimization minimization method to solve the weights, and the value appearing after the current tendency is predicted according to the weights; in the prediction result processing step, a prediction result is subjected to exponential transformation. According to the website page view prediction method, the known current tendency is compared with the historical tendencies for calculation of the correlation coefficients, the deviation of the current tendency is estimated according to all the historical tendencies, the weight of each historical tendency is selected through the optimization method, estimations at all dates are superposed according to the weights, and the current tendency and a follow-up tendency can be predicted according to a superposition result, so that the deviation of the estimations of the current tendency by synthesizing the historical tendencies is minimum, and reliable prediction is achieved.

The invention discloses a non-local mean image de-noising method with a noise intensity self-adaptation function. The non-local mean image de-noising method with the noise intensity self-adaptation function comprises the steps that firstly, a gray scale strip image is collected, and de-noising processing is conducted on the gray scale strip image by using different de-noising intensity parameters through a non-local mean method, so that optimal de-noising intensity parameters under the condition of different degrees of brightness are obtained; then, optimal de-noising intensity parameters corresponding to other degrees of brightness are calculated through a linear interpolation method; finally, de-noising processing is conducted on the image in a logarithm domain through the optimal de-noising intensity parameters corresponding to different degrees of brightness, and exponential transformation is conducted on the de-noised image in the logarithm domain, so that a final de-noised image is obtained. The non-local mean image de-noising method overcomes the defect that in an existing method, de-noising intensity parameters are fixed, and the de-noising effect of the image is improved; due to processing in the logarithm domain, the difference of brightness of pixels in a dark region can be increased, the difference of brightness of pixels in a bright region can be reduced, and the de-noising effect of the image can be improved.

The invention relates to a small aircraft attack angle sliding mode tracking method based on an inverse solution transfer function, and belongs to the field of rehabilitation robots. The method comprises the following steps: measuring an attack angle of an aircraft, forming an attack angle error signal with an instruction signal, and performing softening and exponential transformation to obtain asoftening and exponential error signal; carrying out integration to obtain a corresponding integration signal; then carrying out filtering differential to obtain an attack angle differential signal, and measuring a pitch angle rate signal to carry out linear combination so as to obtain a sliding mode surface signal; forming an equivalent nominal control item according to nominal model information,and forming a combined control quantity together with nonlinear transformation of a sliding mode surface and the attack angle error signal; and then through a nonlinear inverse solution transfer function method, weakening flutter brought by sliding mode control, generating a pitch channel control law, and realizing attack angle tracking control of the aircraft. The method has advantages of high attack angle tracking precision, a high anti-interference capability, small flutter and a good attack angle tracking dynamic effect.

The invention discloses an SAR (Synthetic Aperture Radar) image speckle reduction method based on sparse representation, which mainly solves the problems of incomplete detail information and unsmooth homogeneous regions for SAR image speckle reduction in the prior art. The method comprises the following steps of: carrying out logarithmic transformation on an original SAR image Y; carrying out overlapping blocking on an image Y' subjected to logarithmic transformation; taking the own information of the image blocks as control factors and carrying out sparse representation on the control factors; carrying out dictionary learning by adopting an approximate KSVD (K Singular Value Decomposition) algorithm to obtain a self-adaptive dictionary and updated sparse representation coefficients; obtaining an image W by adopting the self-adaptive dictionary and the updated sparse representation coefficients, and carrying out exponential transformation on the image W to obtain an image R; and carrying out nonlinear anisotropic diffusion on a difference image V obtained from the original SAR image Y and the image R subjected to the exponential transformation, thereby obtaining a final speckle-reduced image. The method disclosed by the invention has the advantages that the homogeneous regions of speckle reduction results are smooth, point targets are clear and visible, and edge information iskept complete and the like, besides, the method is suitable for a preprocessing process of SAR image understanding.

The invention provides an ultrasonic image noise suppression method which comprises the steps of: 1, carrying out logarithmic transformation on an ultrasonic image to obtain a logarithmic image; 2, extracting an edge signal area of the logarithmic image; 3, carrying out non-downsampling contourlet transformation to obtain a plurality of high-frequency sub-bands; 4, extracting an edge signal area and a signal stable area of each high-frequency sub-band; 5, carrying out noise reduction on each high-frequency sub-band in the edge signal area and the signal stable area; 6, carrying out non-downsampling contourlet inverse transformation on the high-frequency sub-bands subjected to noise reduction; and 7, carrying out exponential transformation on the image obtained in the step 6 to obtain a final noise reduction image. By adopting the noise reduction method provided by the invention, speckle noise can be effectively suppressed, detail information in the ultrasonic image is reserved largely, and a better noise reduction effect is achieved.

The invention belongs to the technical field of machine vision, and particularly relates to an infrared target detection method based on target boundary positioning. In the algorithm, firstly, the contrast of an infrared image is enhanced by using adaptive exponential transformation, and semantic details of the image are enhanced; the improved composite Resnet-50 is adopted to extract characteristic spectrums under different scales in the image; feature fusion is carried out by adopting a bidirectional feature pyramid, and the detection capability of the model on weak and small targets is enhanced; and Focal Loss is adopted as a classification loss function of the model, a loss function based on target boundary positioning is adopted as a regression loss function of the model, and the detection model is trained. According to the invention, effective detection of the infrared target is realized.

The invention discloses an illumination normalization method for processing human face images, which is specifically implemented according to the following steps: read in the human face image to be processed and take logarithm of it; calculate the shadow layer image; calculate the reflective layer image and perform exponential transformation ; select the sample image and calculate its histogram; normalize the reflective layer image by using the histogram matching method to obtain the corrected image of the face image to be processed by the illumination normalization method. The feature information on the large-scale layer can be preserved by using the edge-preserving filter; the sample image in the visually optimal area is used, so that the mean and variance of the corrected reflection layer image can be in the visually optimal area, which is beneficial to the subsequent face The recognition system can improve the correct rate of face recognition, and solve the problem that the existing feature extraction method of invariant illumination causes a large loss of feature information during face recognition, which is not conducive to the subsequent face recognition.

The invention discloses a noise suppression method used for a synthetic aperture radar (SAR) image of a city region. The method comprises the following steps of estimating Alpha-Stable distribution parameters for sample data in sliding windows on real part / imaginary part data of the SAR image, and recording the parameters of all the sliding windows; performing logarithmic transformation on amplitude data of the SAR image, defining sliding windows corresponding to a real part / an imagery part on the image subjected to the logarithmic transformation, and calculating an Alpha-Stable distribution expression and a noise distribution expression in each corresponding window; calculating a posterior probability of a product of the Alpha-Stable distribution expression and the noise distribution expression, and taking the value of a maximum posterior probability in a dynamic range as the value of a central pixel of the sliding window; and performing exponential transformation on the values of the central pixels of all the sliding windows subjected to maximum posterior estimation, thereby obtaining a filtered amplitude image. According to the technical scheme, speckle noises of the SAR image of the city region are suppressed more effectively, so that the quality of the SAR image is improved.

The invention discloses a noise intensity self-adaptive non-local mean image denoising method. Firstly, the gray scale bar image is collected, and the non-local mean method is used to denoise it using different denoising intensity parameters to obtain the best image under different brightness. denoising intensity parameters; then use the linear interpolation method to calculate the optimal denoising intensity parameters corresponding to other brightness; finally use the optimal denoising intensity parameters corresponding to different brightnesses to denoise the image in the logarithmic domain, and the The image after digital domain denoising is subjected to exponential transformation to obtain the final image after denoising. The present invention overcomes the shortcomings of fixed denoising intensity parameters in the existing methods, and improves the denoising effect of the image; at the same time, processing in the logarithmic domain helps to increase the difference in pixel brightness in dark areas, reduce the difference in bright areas, and more It is beneficial to improve the denoising effect of the image.