51results about How to "Keep edge information" patented technology

The invention provides a method to eliminate noise, comprising the following steps: to compare a central pixel point and a neighborhood middle pixel point; to determine whether the central pixel point is a noise point based on the comparison result; if the central pixel point is a noise point, to replace the pixel value of the central pixel point with a weighted average of the pixel value of the neighborhood middle pixel point; if not, to replace the pixel value of the central pixel point with a weighted average between the pixel value of the neighborhood middle pixel point and the pixel value of the central pixel point. The method effectively eliminates the noise and well maintains the image edge information.

The invention discloses a fundus image blood vessel segmentation method based on phase congruency and mainly overcomes the defect that a traditional method can not be used to accurately segment blood vessels in fundus images. The fundus image vascular segmentation method base on the phase congruency can be simultaneously used to segment small blood vessels of most tips. The method comprises the steps: (1) extracting green channels of the fundus images, (2) enhancing the contrast ratio of the images through contrast limited adaptive histogram equalization (CLAHE), (3) filtering the fundus images through the anisotropic coupled diffusion equation, (4) segmenting the blood vessels of the fundus images filtered or not filtered through the anisotropic coupled diffusion equation in a phase congruency algorithm, (5) multiplying pixel-levels of results of vessels, of two fundus images, segmented based on the phase congruency algorithm, (6) processing the images in a binaryzation mode through the iterative threshold segmentation method, (7) optimizing the images in the mathematical morphology method. The fundus image vascular segmentation method has significant application values in fields of three-dimensional splicing of the fundus images and judging existence of diabetes mellitus and severity of diabetes mellitus.

The invention belongs to the analysis field of quantitative metallography on all-form crystal grains in a steel material microstructure and particularly relates to an automatic measurement and fine classification method for steel crystal grains, and a system thereof. According to the method, an image acquisition device acquires the original images of to-be-measured crystal grains of the steel material firstly, and then the original images are pre-processed by an image pre-processing module. The pre-processed images are subjected to the region labeling treatment by an automatic measurement module, and then the images of to-be-measured crystal grains can be obtained. After that, the geometry characteristic parameters of the images of to-be-measured crystal grains are extracted, and then the characteristic morphological parameters of target crystal grains are measured through the random field area algorithm. The area of crystal grains is obtained, and then the grain size of crystal grains and the mixed crystal degree (GME) of crystal grains can be figured out. The mixed crystal degree (GME) of crystal grains is automatically classified by an automatic classification module according to a most suitable threshold. In this way, the blank in measuring and classifying the mixed crystal degree of steel crystal grains in the prior art can be filled up. Meanwhile, the characterization precision of the images of steel crystal grains is up to plus / minus 0.001 [mu]m. Therefore, by adopting the above method and the above system, the characterization precision of the images of steel crystal grains is highest during the steel metallographic structure analysis process.

The invention discloses a weighted synthetic kernel and TMF based polarimetric SAR image classification method and relates to polarimetric SAR image classification. The method comprises the steps of step 1, selecting polarimetric SAR image polarization characteristics and training samples, and building characteristic space; step 2, establishing the weighted synthetic kernel; step 3, achieving initial classification by the combination of the weighted synthetic kernel and a support vector machine to be used as an initial value of a marking field X; step 4, estimating a novel marking field X and a novel auxiliary field U; step 5, using the marking field X as the final polarimetric SAR image classification result till the marking field X converges. According to the method, the problems that the initial classification accuracy is not high, and the Markove field cannot process polarimetric SAR image unsteady characteristics of the prior method are mainly solved. Homogeneous region classification results are smooth, marginal information can be kept well, the classification accuracy is improved apparently, and the method can be used for target detection and recognition of polarimetric SAR images.

The invention belongs to the field of digital image processing, and in particular discloses a scene motion blurred image restoration method in the presence of a moving object. The method comprises the following steps of: 1, estimating and amending parameters of a motion model of a scene motion blurred image and extracting a moving object blurred image; 2, restoring the motion blurred image: including restoring the scene motion blurred image and restoring the moving object blurred image, wherein the parameters of the motion model of static scene motion blur are estimated by the method in the step 1 and are amended and then the image is restored by using an image restoration method for the scene motion burred image; and 3, fusing restored images: embedding the moving object image restored by the step 2 into the restored scene image, namely fusing the restored moving object image and the restored scene image, and processing the joint part of the restored moving object image and the restored scene image by using a filtering method to enable a joint of the images to be smooth so as to keep integrity of the image.

The invention belongs to the field of machine vision, and particularly relates to a 3D scene flow estimation method based on the self-adaptive non-local smoothing method. The method comprises the steps of according to the corresponding relation between stereoscopic image sequences acquired by a binocular camera, combining the local constraint method with the global smoothing method and introducing the self-adaptive non-local smoothing method; in reference to a Lucas model, designing scene flow data items for the local neighborhood constrains; adopting the robust function as a smooth item to construct a total variation smoothing approximate to an L1 norm; and solving out an energy function in the de-antithesis manner. According to the technical scheme of the invention, noise-induced heterogeneous points in an image sequence can be effectively removed, and the edge information of the motion field can be maintained. Meanwhile, the information can be effectively transmitted to a low-texture region.

A hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization comprises the following steps: 1) obtaining a hyperspectral data image to be denoised, N being additive white Gaussian noise (AWGN), X being a recovered clean image, m and n being the length and width of the spatial dimension of the hyperspectral image respectively, and p being the number of spectral bands; 2) constructing a multi-channel truncated nuclear norm and total variation regularization hyperspectral image denoising model; (3) optimizing the model by adopting Alternating Direction Method of Multipliers (ADMM); and 4) outputting the denoised hyperspectral image. The method has the advantages that the piecewise smooth prior is better reserved, the edge information is effectively kept, and the denoising effect of Gaussian noise is enhanced at the same time.

The invention discloses a human body animation synthesis method based on a human body soft tissue grid model, and the method comprises the steps: firstly extracting the features of different dimensions of an image through a feature pyramid network, and then inputting the features of different dimensions into subsequent tasks such as human body part segmentation, soft tissue texture and posture estimation through a region-feature alignment technology.; and according to the three-dimensional posture and the soft tissue texture, mapping the figure in the original image to a three-dimensional human body soft tissue grid model to achieve reconstruction of the human body soft tissue grid model, and then achieved human body animation synthesis through redirection of the human body soft tissue grid model. By using the method of combining the human body soft tissue mesh model and the deep learning, the human body soft tissue motion can be accurately captured, so that the synthesized human bodyanimation details are richer in expression.