420results about How to "Valid reservation" patented technology

The invention discloses an image super-resolution reconstruction method based on a multi-band deep convolutional neural network, comprising the following steps: selecting training samples and test samples; inputting training images to the network in batches for feature extraction, and combining long-term and short-term memories to construct a multi-band learning structure, then performing featurerefinement, feature mapping and up-sampling reconstruction; and obtaining network parameters according to the model obtained through training to complete the image reconstruction. The invention enhances high-frequency information reconstruction by introducing multi-band learning, and uses the memory migration operation to make the network have long-term and short-term memories at the same time, which speeds up the image reconstruction speed, enhances image edge and texture detail reconstruction, and obtains image super-resolution reconstruction results of better quality. The invention has strong super-resolution capability, and the reconstructed image is closer to the real image.

The invention provides a preparation method of a mesoporous silicon dioxide hollow sphere, relates to a silicon dioxide hollow sphere, and provides a preparation method of a mesoporous silicon dioxide hollow sphere. The method comprises the steps of: adding a silicon dioxide sphere into water, and ultrasonically processing to obtain silicon dioxide sphere dispersion solution; adding a cationic surface active agent into the obtained silicon dioxide sphere dispersion solution, adding an alkali source, and etching in a stirring way; and collecting, cleaning and drying a precipitate after etching to obtain white powder, and removing the cationic surface active agent out of the white powder to obtain the mesoporous silicon dioxide hollow sphere. The silicon dioxide sphere is taken as a template, and the mesoporous silicon dioxide hollow sphere is prepared by means of carrying out alkali etching under the effect of the cationic surface active agent. The preparation method has the advantages of being strong in operability, low in cost, simple in reaction device, mild in preparation processing condition, cleaning and pollution-free in reaction processe, and high in reaction efficiency and the like. Compared with other synthesis method, the preparation method is good in industrial application prospect.

The invention, which belongs to the technical field of computer vision images, discloses a local-information-based infrared weak target detection method in a complex background. According to the invention, a filtering structure is constructed by using one point v0 in a to-be-detected image as the center, wherein the filtering structure includes a protection frame and a filtering window; a plurality of directions that use the point v0 as the center and point to the filtering window are found out, wherein the plurality of directions are evenly distributed; weighted gray values from the protection frame to the filtering window in the multiple directions are calculated respectively and one value closest to the point v0 gray value among obtained results is used as a background estimation valueof the point v0; the filtering window slides in the to-be-detected image to obtain background estimation value of all points and a background estimation image is formed; the background estimation image and the to-be-detected infrared image are differentiated to obtain a difference image; and then target segmentation and extraction are carried out on the difference image to obtain a target detection result. With the method provided by the invention, clutter interferences of the edge information and the edge intersection part in the complex background can be suppressed effectively; adaptation tothe bright and dark targets is improved; and the target detection effect is improved effectively.

The invention discloses an SAR image speckle suppression method based on second generation curvilinear wave transformation, which mainly overcomes the defect of scratch effect and point target loss brought by a curvilinear wave to the SAR image speckle suppression. The SAR image speckle suppression method comprises the following steps: performing the second generation curvilinear wave transformation to a selected test image and partitioning the selected test image into 5 layers of subbands; keeping coefficients of the first layer unchangeable and zero-setting coefficients of the fifth layer; respectively evaluating parameter vectors of hybrid Gaussian models from the second layer to the fourth layer by an EM method; marking the coefficients from the second layer to the fourth layer; reconstructing the image, detecting the edge of the reconstructed image, and performing the average filtering to the uniform area of the reconstructed image to obtain the filtered image; and performing the nonlinear anisotropy dispersion iteration to a difference image obtained by the original image and the filtered image to obtain a speckle suppressed image. The invention has the advantages of keeping clean edge of the image, removing the scratch effect and remaining the point target characteristic information of the image, and can be used for preprocessing scene analysis and image understanding in the SAR image.

The present invention discloses a preparation method for a leisure bean product, ie., multi-layer bean rolls. The purpose of the present invention is to overcome disadvantages of single variety, rough mouthfeel, poor toughness and the like of the existing leisure bean product. The main process steps of the method of the present invention comprise: selecting materials, soaking and cleaning, refining, centrifugating and filtering, carrying out continuous slurry boiling, carrying out slurry curdling, beating into fine slurry, carrying out first molding, carrying out alkalization seasoning, carrying out second molding, and carrying out secondary seasoning. According to the present invention, the traditional bean product processing technology and the modern food production technology are effectively combined, and the production process is optimized, such that problems of alkalization molding, seasoning, retention of soybean nutrients and the like are solved, and the technical level of the products is effectively improved; the product of the present invention has characteristics of delicate tissue, high elasticity, moderate hardness, unique flavor, rich nutrition, convenient eating, health and safety, easy carrying and storing, and the like, and is suitable for various consumer groups.

The invention relates to an image noise eliminating method for preserving high frequency information, and is characterized in that: comprising the two following steps that: (a) linear filtering processing is done to an inputting image with random noise and a filtering function: (b) conjugate gradient optimization restoration operation is done by taking the filtering result of the frontal step as a degenerated function, wherein a first step is to fully eliminate the random noise in the image, a second step is to restore the high frequency information such as a losing image edge, details and so on of the first step. Therefore, the image noise eliminating method of the invention is capable of fully eliminating the image random noise on the premise of preserving the image high frequency information.

The invention discloses a making method of semi-fermented fruit wine. The fruit wine is produced in a base wine immersion and fermentation mode; fruits are immersed to dissolve nutrition components and pigments in the fruits in order to effectively reserve the fruit aroma, reach oxidation prevention and color protection effects and realize sterilization and bacteriostasis, and fermentation is carried out to increase the mellowness of the fruit wine an make the wine have plump and soft mouthfeel, so the semi-fermented fruit wine has mellow and plump soft mouthfeel and well reserves the color and the rich aroma of the original fruits, and the defects of two traditional fruit wine processing technologies are effectively overcome, thereby the fruit wine has high nutrition values and good mouthfeel, the health efficacy of the fruit wine is in favor of improving people's health, and the fruit wine is easily accepted by consumers. The raw materials of the fruit wine have wide selection range, resources are fully utilized, and seasonal fruits can be selected to make the fruit wine, so the cost is low; and the method has the advantages of simplicity, easiness in operation and short production period, and the fruit wine is a low-alcohol degree alcohol and beverage integrated health fruit wine, and has wide application prospect.

The invention provides an image obtaining method and an image obtaining device. The method and the device can realize multi-focus shooting and processing on shooting subjects with different depths of field, and images with a high clear degree are obtained. The method provided by the invention comprises the following steps that: a plurality of regions to be focused are recorded according to received selection instructions of the regions to be focused; each region to be focused is subjected to focal plane contrast ratio calculation; then, automatic focusing is carried out according to the focal plane contrast ratio, and a plurality of focal lengths to be focused are obtained; shooting is performed by referring to the plurality of focal lengths to be focused, and a plurality of shot images are obtained; each shot image is divided into a plurality of sub images in the same meshing mode; the plurality of sub images corresponding to each mesh region are subjected to sub image contrast ratio calculation; for the plurality of sub images corresponding to the same mesh region, the sub image with the highest sub image contrast ratio is selected to be used as a fusion sub image corresponding to the mesh region; and the fusion sub images corresponding to all mesh regions are spliced, and a fusion result image is obtained.

The invention provides a method and system for extracting a significant target region of an image based on iterative sparse representation. Firstly, superpixel segmentation is performed on an originalimage by using an SLIC segmentation method with a plurality of groups of different numbers of pixel element parameters to generate a group of split images with different sizes of superpixel regions;for segmentation results of each scale, taking classical visual attention detection results as the initial saliency map constrained foreground and foreground sample region selection, calculating reconstruction residual of each superpixel region as a significant factor by the sparse representation process, optimizing significant test results under the single dimension in combination with recursiveiterative operations, and finally obtaining final significant targets and test results through multi-scale saliency map fusion. According to the method and system for extracting the significant targetregion of the image based on iterative sparse representation, the shortcomings of inconsistency in the single-target saliency evaluation, the difficulty in detection of an image edge significant target, and the incomplete extraction of multiple significant targets in a conventional method are effectively overcome.

The present invention provides a gas concentration prediction method based on an adaptive modularization neural network, and relates to the mine gas concentration detection technology field. The method comprises: collecting the gas concentration data, storing the gas concentration data in a gas concentration database, performing adaptive noise removing processing of the gas concentration data in the database to take the gas concentration data after the adaptive noise removing processing as chaotic time series, establishing the training sample set of an adaptive modularization neural network, constructing an adaptive modularization neural network soft measurement prediction model, and predicating the gas concentration by employing the constructed predication model according to the newly obtained gas concentration data and the historical data in the gas concentration database. The gas concentration prediction method based on the adaptive modularization neural network has a significant noise removing effect, can retain the useful information in the gas concentration time sequence while effectively removing the noise and can construct the soft measurement predication model of the adaptive modularization neural network, and the input information is integrally processed through a plurality of different submodels so as to improve the learning precision and the generalization performance of the predication model and improve the robustness of the prediction model.