36results about How to "Increase information entropy" patented technology

The invention relates to a human face image fusing method based on laplacian-pyramid. On a basis of the laplacian-pyramid decomposition, the invention applies a partial binary mode arithmetic operator into a fusion rule. As for infrared and visible-light human face images in each layer of the laplacian-pyramid, average gradient of the image, standard deviation of an image histogram and Chi square statistic similarity measurement are used for determining the selection of respective weighting coefficients of the infrared and visible-light human face images, and effective fusion is performed by utilizing the determined weighting coefficients so as to obtain a fused image which is rich in detail information, clear and stable and facilitates the observation of human eyes. Compared with visible-light images and near-infrared images, the fused information provided by the invention has greater comentropy but smaller average cross-entropy and mean square root cross-entropy and has more remarkable effect of image fusion.

The invention belongs to the field of infrared image processing, and relates to an image enhancement method based on long-wave infrared polarization feature extraction and fusion method. The method comprises the steps of (S1) collecting an infrared polarization image, and preprocessing the collected infrared polarization image; (S2) performing Stokes polarization state calculation on the preprocessed image in the step (S1), and calculating a polarization degree and a polarization angle according to a Stokes polarization state calculation result; (S3) carrying out completely polarized light decomposition calculation by using the polarization degree and the polarization angle; (S4) defining a polarization orthogonal difference value, setting a weighting coefficient, and extracting a polarization feature image, wherein the polarization feature image comprises a polarization parallel component image and a polarization vertical component image; and (S5) fusing the polarization feature imageand an infrared intensity image by using a non-downsampling shear wave algorithm. The method can effectively extract the polarization information of a target scene, has a good polarization feature extraction effect, performs fusion on the extracted polarization feature image and the infrared intensity image and significantly enhances the information content of the target scene.

The invention discloses a text feature extracting method based on inter-class distinctness and intra-class high representation degree. The method comprises the following steps: preprocessing a training set text; calculating the class distinctness of each feature word through an improved feature selecting method so as to select feature words with more class representation, wherein the selected feature words are of high distinctness among different classes; and further screening the selected feature words which are of high class distinctness based on the intra-class distribution rate and information gain (IG) of the feature words. With the adoption of the method, the feature selection is carried out twice to select the feature words which are of high intra-class information entropy and high intra-class distribution rate, and thus the classifying efficiency and accuracy can be improved; in addition, the calculation is simple, so that the text classifying speed and accuracy can be improved.

The invention relates to a device and a method for classifying plate, belongs to the technical field of image processing and solves the problems that existing plate classification methods are low in precision and poor in interference resistance. The device for classifying plates comprises a light source system, an image acquisition system and a narrow band optical filter. The light source system is used for projecting multi-wavelength light beams for the plates to be detected, and the image acquisition system is used for acquiring the to-be-detected plate grey level images which are filtered by the narrow band optical filter and sending the grey level images to a computer. The method for classifying the plates includes the steps: acquiring the grey level images with different wave bands of the plates to be detected; determining the wave band with the best separability by utilizing standard plates of the known wood assortments, and classifying the plates to be detected by utilizing the wave band with the best separability; or performing pixel-level image fusion for the grey level images with different wave bands, and acquiring a composite image; and classifying the plates to be detected by utilizing the composite image. By the aid of the device and the method for classifying plates, plate classification precision and interference resistance are increased.

The invention discloses a method for dynamically calibrating image light intensity on the basis of environmental factors. The method includes the steps of reversing an image to distinctly display key areas; segmenting the image into NxN sub-blocks for processing; processing the sub-blocks via power transformation; processing the sub-blocks via grayscale mapping transformation; subjecting the sub-blocks to histogram processing; processing the sub-blocks via dynamic fuzzy histogram equalization; respectively processing the sub-blocks via masking and filtering by a hybrid space processing method; iterating every sub-blocks of the NxN sub-blocks by a Lucy_Richardson filter; respectively calculating to optimize the power transformation step and the dynamic fuzzy histogram equalization step of the sub-blocks by a fuzzy logic theory; and reversing the image again and outputting the final image, wherein the N is an integer nine, the maximum information entropy is judged as the optimum optimizing standard. By the aid of the method, information entropy of the image is effectively increased, and good light calibrating effect is obtained.

The invention discloses an infrared remote sensing image super-resolution reconstruction method based on TGV regularization. The method comprises the steps that a remote sensing infrared image super-resolution reconstruction model based on TGV regularization is obtained; a Huber function is utilized to perform semi-quadratic regularization to split the remote sensing infrared image super-resolution reconstruction model based on TGV regularization, so that a split image super-resolution reconstruction model is obtained; and an alternate direction multiplier method is utilized to perform value solving on the split image super-resolution reconstruction model to generate a super-resolution reconstruction image. Through the method, a noise reduction function can be achieved, good detail maintaining capability is obtained, and a peak signal-to-noise ratio, a signal-to-noise ratio and evaluation index values of an average structure similarity, information entropy, etc. can be effectively increased.

The invention relates to an image recognition method and system. The image recognition method includes the following steps that: objects of which the area is within a preset range are tracked and photographed, so that image data can be obtained; the image data of the same object are sequenced based on image quality obtained through photographing; image data of which the image quality is within a preset ranking preset range are stored after each sequencing; and the stored image data are processed, so that the feature data of the photographed object can be extracted. With the image recognition method and system adopted, the efficiency of image recognition and the correctness of the recognition can be improved.

The invention discloses a self-adaptive exposure adjustment multi-scale entropy fusion underwater image enhancement method, which comprises the steps of applying generalized bounded logarithm additionor subtraction operation to an original underwater image to derive derivative images with different exposures; calculating multi-scale Gaussian pyramid images with different exposure quantities and multi-scale Laplace pyramid images with different exposure quantities; calculating multi-scale Gaussian pyramid weight images with different exposures; obtaining a normalized multi-scale Gaussian pyramid weight iteration image with different exposure quantities; obtaining a multi-scale pyramid fusion image; obtaining a pyramid fusion image; and obtaining an enhanced image based on rich gradient information of the pyramid fusion image. According to the method, the underwater image is enhanced, so that the contrast and saturation of the enhanced image can be well considered, image details are richer and clearer, and the contrast, information entropy, color information and the like of the whole image can be further improved.

The invention discloses an area energy photon counting image fusion algorithm based on an adaptive threshold value, and belongs to the technical field of image fusion. The method is characterized by comprising the following steps of step 1001, carrying out redundant wavelet transformation; step 1002, calculating regional energy, neighborhood mean square error, matching degree and self-adaptive threshold value; step 1003, calculating a weighting coefficient and an enhancement factor; step 1004, calculating a matching degree; step 1005, judging a matching degree and a self-adaptive threshold value; step 1006, when the matching degree is smaller than a self-adaptive threshold value, selecting an enhancement value of a pixel point of an area with high energy as a corresponding pixel point value in the fusion image; step 1007, if the matching degree is greater than or equal to a self-adaptive threshold value, adopting a weighted area energy method; and step 1008, obtaining a photon countingfusion image. Through the algorithm, the problems of unclear contour, serious noise, photon counting image information redundancy under extremely weak illumination, unclear details and the like of afusion image obtained through a regional energy fusion method are solved.

The invention discloses a polarization image visualization method based on color image fusion. The method comprises the following steps: firstly, converting a polarization image into a three-channel image of light intensity, polarization degree and polarization angle; secondly, decomposing the image into a polarized light color image and a natural light grayscale image; thirdly, performing image fusion on the two decomposed images, specifically, selecting a proper edge-preserving fusion algorithm according to requirements to fuse light intensity channels of the two images; then, normalizing the fused light intensity and polarization angle channels; and finally, projecting the normalized image to an HSV color space to obtain a final polarized color image with visualization. According to theinvention, highlighting of low-polarization-degree high-frequency detail features in an original image is used as a solution for improving image information amount and readability.

The invention discloses an underwater image enhancement method based on adaptive fractional order multi-scale entropy fusion. The underwater image enhancement method comprises the steps: dividing an original underwater image into non-overlapping image blocks; establishing an order reference matrix of the fractional order of the gradient domain; constructing a multi-scale fractional order referencematrix; obtaining a fractional order-based enhanced output image consistent with the scale number; calculating the information entropy and contrast of the fused image; determining an output image with enhanced contrast in the image block; obtaining a global contrast enhanced image; converting the original underwater image from the RGB image into a grayscale image; solving an adaptive gain function of the gradient image; obtaining a final adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion; and outputting an adaptive gain underwater enhanced image basedon fractional order multilayer entropy fusion. The adaptive fractional order differential image enhancement method has a good effect on image enhancement of a texture region, and can adapt to changesof image gradient and brightness, so that the image enhancement effect is better.

The invention discloses an adaptive gain image enhancement method based on fractional order multi-scale entropy fusion. The adaptive gain image enhancement method comprises the steps: dividing an original underwater image into non-overlapping rectangular image blocks; obtaining enhanced output images which are consistent with the scale number and based on the fractional order; calculating the information entropy and contrast of the fused image; determining an output image with enhanced contrast in the image block; obtaining a global contrast enhanced image; converting the original underwater image from the RGB image into a grayscale image; calculating a gradient image corresponding to the grayscale image, and solving an adaptive gain function of the gradient image; calculating a final adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion; and outputting an adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion.According to the adaptive gain image enhancement method, enhancement processing is carried out on the underwater image, so that details of the enhanced image are richer and clearer, and the contrast ratio, information entropy, color information and the like of the whole image can be further improved.

The invention discloses a medical digital subtraction image fusion method based on ridgelet transformation, comprising the following steps of: (1) respectively carrying out the ridgelet transformation on two images to obtain ridgelet transformation matrixes; (2) fusing; (3) carrying out ridgelet inverse transformation on the fused ridgelet transformation matrixes to obtain a reconstructed image, i.e. a fused image. The invention is characterized in that in step (1), setting an initial judging threshold value and a step length for carrying out the ridgelet transformation, reconstructing the fused image by utilizing inverse transformation, calculating the information entropies of the fused image, changing the judging threshold value by using a dynamic fuzzy method according to the step length, repeating the operation, and then obtaining the ridgelet transformation matrixes in step (1) by using the judging threshold value corresponding to the maximum information entropy as the final judging threshold value. The invention can effectively improve the information entropies of the fused image, lower the error rates of mean square roots and has the performance better than that of other traditional fusion method. The algorithmic time has low complexity, the obtained result is more accurate, and therefore, the invention greatly enriches the grain details of medical images.

The invention relates to the field of file transmission, and particularly provides a file stream byte group data encryption and network transmission method. The file stream byte group data encryptionand network transmission method comprises the following steps: reading a file to be encrypted in a file stream manner to obtain a file stream byte array a of the file; performing a round of exclusiveOR processing on the array a and the logistic mapping iteration value to obtain a new file stream byte array b; scrambling the array b, sorting the sequences by using a logistic iterative sequence, and replacing the array b according to the logistic sequence to obtain a new byte array c; and performing BASE64 encoding on the finally obtained byte array to obtain a character string to be transmitted. Compared with the prior art, encryption is carried out from the byte stream level, so that the converted new coded bytes have higher information entropy and lower byte correlation degree and are used for resisting statistical attacks and inscription attacks, a guarantee is provided for network transmission security, and the method has good popularization value.

The invention relates to the technical field of data anonymity, in particular to an anonymity method based on sensitive hierarchical information entropy, which comprises the following steps: acquiring a published data set; establishing an anonymous model based on the published data set; establishing an information loss measurement standard model based on the information entropy of the published data set; traversing the published data set based on the information loss measurement standard model to obtain a maximum tuple with the maximum privacy security index, and adding the maximum tuple into a corresponding equivalent group in the anonymous model; and replacing the value of the maximum tuple with the quasi-identification attribute center of the equivalent group to obtain an anonymous data set, and processing the published data set by introducing an information loss measurement standard model established by the information entropy and an anonymous model to obtain the anonymous data with the highest information entropy, namely the highest data availability. The problem that an existing anonymous method is low in data availability after data anonymization is solved.

The invention discloses an end-to-end key generation method based on channel fingerprints and auxiliary data. The method comprises the steps that a sending end generates a random key; The sending endtraverses the channel characteristic data , and judges whether the data falls into a quantization interval or not; the sending end quantizes the channel characteristic data by using a zero-leakage quantization method; the sending end obtains an auxiliary data generation function, calculates an auxiliary data value and sends the auxiliary data value to the receiving end; the receiving end traversesthe auxiliary data and restores the channel characteristic data; and the receiving end judges whether the restored channel characteristic data is equal to the channel characteristic data detected bythe receiving end or not, if not, the secret key is restored directly, if yes, the secret key is restored according to the maximum likelihood algorithm, and finally the generated secret key is obtained. According to the invention, channel characteristic data processing, secret key generation and corresponding auxiliary data extraction are carried out through a corresponding algorithm, so that therandomness and consistency rate of the secret key of the end-to-end communication equipment are enhanced, and higher communication security is ensured.

The invention discloses an underwater illumination non-uniform image enhancement method based on an alternating direction multiplier method, and the method comprises the steps of converting an image from an RGB color space to a Lab color space when an initial illumination image is estimated, and extracting a brightness component as the initial illumination image; then, on the basis of an alternating direction multiplier method principle, constructing a constraint optimization equation of an augmented Lagrange framework, and solving the optimization equation through iteration to obtain a precise illumination map; according to the Retinex theory, converting an image to be processed and an illumination image to a logarithm domain to obtain a reflection image in the logarithm domain, and then carrying out exponential operation to obtain an enhanced image; and finally, inhibiting underwater noise by using a bilateral filter to obtain a finer underwater enhanced image. According to the method provided by the invention, the quality of the underwater illumination non-uniform image is effectively improved, and compared with similar methods, the method provided by the invention has better performance in the aspects of removing the influence of illumination on the underwater image and retaining image details.