147 results about "Pyramid (image processing)" patented technology

The invention provides a three-dimensional terrain model real-time smooth drawing method with combination of a GPU technology, and belongs to the technical field of image processing. The objective of the invention is to provide the three-dimensional terrain model real-time smooth drawing method with combination of the GPU technology so that cache reuse in multiple times of drawing can be realized based on the current popular programmable GPU technology with a global digital elevation model acting as a data source, and load of computation space is effectively reduced. The method comprises the steps of construction of a multi-resolution pyramid model, elimination of image noise points, filtering of images, partitioning of planar projection of the earth according to equal latitude and longitude, and construction of different hierarchical levels of pyramid layers according to a mode from the top to the bottom. Acceleration and enhancement of terrain rendering are realized based on the programmable GPU technology, i.e. all phases of a graphical drawing pipeline are controlled by using shader languages, two and textures are respectively generated by vertex information and index information of elevation data to be stored in video memory for scheduling of whole terrain drawing; and vertex interpolation and migration are performed in the geometric phase by utilizing a curved surface subdivision and fractal technology so that procedural details are generated and the phenomenon of edges and corners of the terrain mesh when resolution is insufficient can be compensated.

The invention discloses a fast color image segmentation method based on combination of clustering analysis and an image pyramid. The method comprises the following steps: 1) constructing a Gauss and Laplace image pyramid according to a source image, and obtaining a low-resolution image and an edge image; 2) transforming the low-resolution image to an HSV (hue, saturation and value) space, obtaining the clustering number and an initial clustering center through histogram analysis, and performing clustering segmentation on the low-resolution image in the HSV space by use of a clustering algorithm; 3) performing up-sampling on the segmentation result obtained in the step 2), projecting to the original resolution, performing spatial filtering and removing the excessively small area to obtain the original resolution area segmentation result; and 4) integrating the edge image obtained in the step 1) and the area segmentation result obtained in the step 3) to obtain the final segmentation result. The method disclosed by the invention guarantees the segmentation quality while increasing the color image segmentation speed, and is suitable for a digital image processing system with high requirements on real-time property.

An image processing module infers depth from a stereo image pair according to a multi-scale energy minimization process. A stereo image pair is progressively downsampled to generate a pyramid of downsampled image pairs of varying resolution. Starting with the coarsest downsampled image pair, a disparity map is generated that reflects displacement between corresponding pixels in the stereo image pair. The disparity map is then progressively upsampled. At each upsampling stage, the disparity labels are refined according to an energy function. The disparity labels provide depth information related to surfaces depicted in the stereo image pair.

The invention discloses a generative adversarial network image defogging method based on a fusion feature pyramid in the technical field of image processing. The technical problems that in the prior art, information of an image processed through an image enhancement defogging method is lost, if parameters of the image processed through an image restoration defogging method are improperly selected,the effect of the restored image can be influenced, and the image defogging speed is influenced through a defogging algorithm based on deep learning are solved. The method comprises the following steps: inputting a foggy image into a pre-trained generative adversarial network, and obtaining a fogless image corresponding to the foggy image, wherein the generator network of the generative adversarial network is fused with a feature pyramid.

The invention belongs to the technical field of medical image processing, and particularly relates to a cancer lesion detection and diagnosis system for early esophageal squamous cell carcinoma of a narrow-band endoscopic image. The system comprises a feature extraction backbone network, a feature pyramid, a region candidate network, a region of interest pooling unit and a cancer focus classification network, and a system for visualization on a narrow-band imaging endoscope image. The backbone network is used for extracting a feature map of an input image; the feature pyramid is used for fusing features of different scales; the region candidate network proposes a possible lesion region; the region of interest pooling unit pools the features to a suspected lesion area; the cancer lesion classification network classifies the cancer lesions; and finally, a narrow-band imaging endoscopic image is visualised, and frame selection marking is carried out on the cancer lesions by using different colors. The image of the narrow-band imaging endoscope is input into the network model, the cancer focus of the early esophageal squamous cell carcinoma existing in the image is detected and diagnosed, the diagnosis efficiency can be effectively improved, and a doctor is assisted in obtaining higher diagnosis precision.

The invention relates to the technical field of digital image processing, in particular to a target detection network and method based on a hybrid cavity convolution pyramid, and the target detection network comprises a backbone network, a hybrid receptive field module, a low-layer embedded feature pyramid module and a detection module. The backbone network uses a hierarchical cascade network structure to extract target picture features; the hybrid receptive field module performs feature enhancement on the highest layer feature map output by the topmost end of the backbone network; the low-layer embedded feature pyramid module is used for downwards fusing high-layer features on the basis of a feature pyramid and generating a final to-be-detected feature map in a low-layer embedding manner; and the detection module is used for positioning and classifying the to-be-detected feature map and outputting a result. Through the target detection network and the target detection method, the problems of missing detection and error detection caused by scales and shielding can be effectively solved.

The invention relates to the technical field of image processing, and provides a surface defect detection method and device. The method comprises the following steps: firstly, extracting each bottom-layer feature of a to-be-detected surface defect image to obtain each feature image pyramid, determining each feature image corresponding to each feature image pyramid according to a center peripheral difference mechanism, normalizing each feature image, and adding the feature images of the same type to obtain each feature saliency map; fusing the feature saliency maps by taking the energy proportion of each feature saliency map as a weight to obtain a synthetic saliency map, then extracting, fusing and sampling high-level features of the surface defect image to obtain a high-level saliency map; and finally, taking the energy proportion of the synthetic saliency map and the energy proportion of the high-level saliency map as a weight, fusing the synthetic saliency map and the high-level saliency map to obtain a total saliency map, and determining defect types and defect positions of the surface defect image according to the total saliency map. According to the invention, the recognition precision of surface defect detection is improved.

The invention discloses a template matching method, system and device based on normalized cross-correlation and a medium. The method comprises the following steps: constructing a template image pyramid and a target image pyramid with the same layer number according to a template image and a target image; performing rotation processing on each layer of image of the template image pyramid according to a preset angle step length to obtain a plurality of rotation images, and further determining key pixel points; screening out a first matching area with the highest correlation coefficient with the rotation image from a top image of the target image pyramid according to the key pixel points, and determining a first upper left corner coordinate and a first rotation angle; and performing layer-by-layer search on each layer of image of the target image pyramid according to the first upper left corner coordinate and the first rotation angle until a second matching area with the highest correlation coefficient with the rotation image in the bottom layer image is determined. According to the method, the calculation amount of template matching is reduced, the template matching efficiency is improved, the template matching accuracy is also improved, and the method can be widely applied to the technical field of image processing.

The invention relates to an image subjective visual effect enhancement method based on a Laplace pyramid, and belongs to the field of digital image processing. The method comprises the steps of 1, performing Laplace pyramid decomposition on an original image to obtain an approximate image and a multi-scale detail image; and 2, enhancing the approximate image based on an improved contrast limited adaptive histogram equalization algorithm; 3, enhancing the multi-scale detail image; and 4, performing Laplace inverse transformation on the enhanced approximate image and the multi-scale detail imageto obtain an enhanced image. Through the steps, the problems of boundary effect, over-enhancement, overlarge noise and the like possibly occurring in the image after traditional contrast-limited adaptive histogram equalization enhancement can be effectively relieved, and the subjective visual effect of the image is remarkably improved.