316 results about "Kernel (image processing)" patented technology

A system and method for computer-assisted karyotyping includes a processor which receives a digitized image of metaphase chromosomes for processing in an image processing module and a classifier module. The image processing module may include a segmenting function for extracting individual chromosome images, a bend correcting function for straightening images of chromosomes that are bent or curved and a feature selection function for distinguishing between chromosome bands. The classifier module, which may be one or more trained kernel-based learning machines, receives the processed image and generates a classification of the image as normal or abnormal.

The invention discloses a license plate positioning and recognition method based on deep learning. An improved YOLO (You Only Look Once) algorithm and an image super-resolution technology are adoptedfor optimization, and an improved YOLO convolutional neural network and a convolutional enhanced SRCNN (Super Recurrent Neural Network) convolutional neural network are trained respectively. Firstly,an improved deep learning YOLO algorithm is adopted to locate a license plate area; a correction detector is used for correcting the detection frame; according to the method, the problem that an existing license plate positioning method cannot perform correct positioning in certain specific scenes is solved, then the enhanced convolutional neural network SRCNN model is utilized to perform super-resolution technology processing on the image of the license plate area so as to obtain the picture with higher resolution and resolution rate, and then the neural network is utilized to perform opticalcharacter recognition. According to the method, when the YOLO convolutional neural network is trained, the maxout activation function is adopted to replace the activation function of the original model, so that the fitting capability is enhanced, Meanwhile, the non-maximum suppression is improved by adjusting the threshold value, so that the screening speed of the bounding box can be effectivelyincreased. When the SRCNN convolutional neural network is trained, the size of a convolution kernel and the number of convolution layers are increased, the image processing effect can be effectively improved, and therefore the method meets the requirements for real-time performance and accuracy.

The invention belongs to the technical field of image processing, and discloses a saturated image deblurring method and system based on a nonlinear degradation model, and the method comprises the steps: inputting a saturated blurred image or a common blurred image and a blurred kernel corresponding to the blurred image; constructing a nonlinear blurred image degradation model according to a saturated image degradation mechanism; constructing a saturated image nonlinear deconvolution framework according to the obtained degradation modeling and the maximum posterior probability framework; determining a priori item, and constructing a nonlinear energy functional model by adopting total variation priori; and solving the nonlinear energy functional through an exchange direction multiplier algorithm, namely an ADMM algorithm or a split Bregman algorithm to obtain a clear image x to be restored. According to the method, the saturated nonlinear degradation characteristic is modeled, blurring in the saturated image is removed, the ringing phenomenon caused by errors of saturated pixels is effectively restrained, and a high-quality clear image is obtained through restoration.

The invention belongs to the field of computer vision and image processing, and relates to a face super-resolution method based on a frequency decomposition multi-attention mechanism. The method comprises the following steps: performing frequency decomposition on an input low-resolution face image by utilizing the reversible property of wavelet transform and inverse transform thereof, constructing a basic module by adopting different kernel convolution aiming at the characteristics of different frequencies, integrating the features of different receptive fields adaptively, and processing the features of different frequencies by using residual attention modules including pixel, space and channel attention mechanisms, wherein low-frequency part textures adopt attention with less calculated amount, and high-frequency part adopts more residual attention modules; applying more networks to a high-frequency part while the calculated amount is kept, extracting and feeding back key points by utilizing a pre-trained face key point extraction network, enhancing contour features, and enhancing the texture features by utilizing a generated resistance network.

The invention belongs to the technical field of image processing, and discloses a multi-scale fine-grained image recognition method and system based on multi-granularity attention. The method comprises the following steps: constructing an attention-based multi-granularity structure, dividing a feature extraction network into a plurality of stages, inputting images with different granularities into different stages of the feature extraction network, and carrying out feature extraction on the image to obtain an original feature map; obtaining attention weights from a channel domain and a space domain for the feature map of each stage through a multi-granularity attention module, fusing the attention weights, and then carrying out weighted fusion on the attention weights and the feature map to obtain key regions of different granularities in different stages; constructing a parallel multi-scale convolution module, grouping the feature maps, independently applying different types of convolution kernels to each group of feature maps, and performing feature extraction on the feature maps with different scales and granularities in different stages; and finally, carrying out feature fusion on the obtained feature map. The relationship between different regions can be fully mined, and low-dimensional space information and high-dimensional semantic information are fused.

The invention belongs to the field of image processing, and particularly relates to a learning-based nonlinear dynamic system deblurring method. The method comprises the steps that firstly, for an image to be deblurred, the kernel estimation energy is controlled by a learnable non-linear dynamic system; secondly, after continuous iteration of a latent image and a fuzzy kernel, great fuzzy kernel estimation is acquired; and finally, the problem of blind deblurring is transformed into the problem of non-blind deblurring, and various existing non-blind deblurring methods can be used for solving.The method has the advantages that 1 a new principle for deblurring is provided, and the learnable dynamic system instead of manually set regularization is used to control kernel estimation; 2 a new structure is designed to learn components in the dynamic system, and a suitable and flexible deblurring system is acquired through the structure; and 3 the method involves a residual network proposed recently, which brings new ideas to image processing and deep learning.

The invention relates to the field of image processing algorithms. According to the technical scheme, the famous high-quality tea picking point position information obtaining method based on machine vision comprises the following steps: 1, obtaining a tea image from a tea garden, and performing Gaussian filtering noise removal on the tea image through a 3 * 3 convolution kernel; 2, setting a respective ROI for each tender shoot obtained through the image; 3) converting the ROI in the RGB color space into an HSV color space, and extracting the features of the tender shoots and the growing point branches of the tender shoots; 4) carrying out secondary binarization segmentation on the extracted tender shoot and branch areas by using an Otsu algorithm; 5) refining the binarized picture in the previous step by adopting an improved Zhang refining algorithm, and extracting a skeleton of the binarized picture; 6) using a Shii-Tomasi algorithm to search bifurcation points of the tender shoots and the branches as feature angular points for detecting the refined skeleton; 7) fitting the lowest point and the angular point of the contour into a linear segment. According to the method, the precision and efficiency of tea tender shoot picking point positioning can be improved.