79 results about "Fusion image" patented technology

The present invention relates to an image fusion method, especially to an image fusion method based on depth learning. The method comprises: employing a convolution layer to construct basic units based on an automatic encoder; stacking up a plurality of basic units for training to obtain a depth stack neural network, and employing an end-to-end mode to regulate the stack network; employing the stack network to decompose input images, obtaining high-frequency and low-frequency feature mapping pictures of each input image, and employing local variance maximum and region matching degree to merge the high-frequency and low-frequency feature mapping pictures; and putting a high-frequency fusion feature mapping picture and a low-frequency fusion feature mapping picture back to the last layer of the network, and obtaining a final fusion image. The image fusion method based on depth learning can perform adaptive decomposition and reconstruction of images, one high-frequency feature mapping picture and one low-frequency mapping picture are only needed when fusion, the number of the types of filters do not need artificial definition, the number of the layers of decomposition and the number of filtering directions of the images do not need selection, and the dependence of the fusion algorithm on the prior knowledge can be greatly improved.

The invention provides a fusion detection method of infrared image and visible light image based on a visual attention model. The fusion detection method comprises the steps that pretreatment is conducted respectively on the collected infrared image and visible light image; based on the visual attention model of human eyes, an interested object can be extracted from the infrared image and the visible light image after pretreatment; with the visible light image after pretreatment being the background, the corresponding infrared image after pretreatment and the visible light image being the background undergo gray-scale image fusion, obtaining a gray-scale fusion image; the gray-scale fusion image undergoes pseudo color mapping marking of the interested object, obtaining and outputting the target pseudo color fusion image. The invention is advantageous in that the problem that the interested object in the prior art is weakened in the fusion image can be resolved, the accuracy and reliability of detection and identification of the interested object can be greatly improved; the difficulty of object identification can be reduced.

Provided is a projection system and a lightness adjusting method thereof. The projection system comprises a plurality of projection devices which project a plurality of images on a screen. The images form a connex set area and have a superposed area. The lightness adjusting method comprises obtaining the images on the screen to obtain image information, selecting a target area from the image information so as to obtain a fused image, corresponding to the images, in the target area, executing a genetic algorithm according to lightness information in the target area to obtain an optimal parameter set and enabling the projection devices to carry out projecting according to the optimal parameter set. The target area is smaller than the connex set area and includes at least one portion of the superposed area. According to the invention, the plurality of projection devices in the projection system can be automatically adjusted to enables the projected images have relatively uniform lightness in the superposed area.

The invention discloses a multispectral stereoscopic vision endoscope device and an image fusion method, and belongs to the field of medical devices. The multispectral stereoscopic vision endoscope device comprises a light source part, a camera part, an image processing part and a display part. The image fusion method comprises the steps that 1, images are acquired by an endoscope image acquisition unit; 2, Gaussian pyramids and Laplacian pyramids are built separately; 3, all layers except the top layers of the Laplacian pyramids of the near infrared image and the green image are processed andfused; 4, near infrared pyramid top-layer images are subjected to target area segmentation; 5, top-layer green images and the top-layer near infrared images are processed and fused; 6, bottom-layer green images are reconstructed according to the Laplacian pyramids obtained after fusion; 7, the reconstructed green images are combined with other channels to obtain final fusion images; and 8, the two fusion images are processed through an anaglyph shifting method to generate a stereoscopic image pair. According to the method, real three-dimensional structure information is obtained.

The invention discloses an ISAR image fusion method based on target characteristics, and relates to fusion of ISAR images. The method includes the steps that firstly, full-aperture data are segmented, and then sub-images are acquired; space characteristic points and space characteristic descriptor vectors of the sub-images are extracted, and then the space characteristic points are matched; then, the optimized space characteristic points are selected; azimuth scale variation and de-centering are conducted according to an azimuth scale factor search method; the rotation angles between the sub-images are calculated, the transformed images are rotated, and the entropy of average range images is acquired; the fusion sub-images are determined through the minimal entropy; finally, base vectors of the fusion images are acquired according to a non-negative matrix decomposition method, and then the final fusion images are determined. Complete information of targets in the fusion images is reserved, and the method can be used for fusion and real-time processing of different sub-images.

The present invention discloses a stage performance assisted training method and system based on augmented reality technology, which are able to detect the standard of an action of a user. The method comprises: respectively acquiring real scene images within view field ranges of a left eye and a right eye of a user wearing a wearable display helmet by using a first camera and a second camera, and acquiring depth image data of the user by using an infrared depth camera for fixing a third view angle; extracting the image data of the user and position data of skeleton joint points of the user; fusing the real scene image with the image data and a preset image of an operator to obtain a fusion image; by utilizing preset position data of skeleton joint points of the operator and the position data of the skeleton joint points of the user, detecting whether the action of the user is standard, and when the action of the user is nonstandard, carrying out marking on a corresponding position of the fusion image; and synchronously displaying the fusion image in a first eyepiece and a second eyepiece of the wearable display helmet in modes which are respectively suitable for left and right eyes to view.

The invention discloses a virtual-real fusion implementation method and system and electronic equipment. According to a virtual-real fusion implementation method, a virtual three-dimensional scene isaccurately copied and constructed according to a real scene through a digital twinning technology, the position and motion of a human body/object in the real scene are positioned, tracked and capturedin real time based on a motion capture technology, an obtained real image is fused into the virtual scene to obtain a virtual-real fusion image which is presented to a user. By applying the virtual-real fusion implementation method provided by the invention, a user can synchronously experience a real interactive operation feeling while obtaining a good visual angle immersion feeling.

The invention discloses a three-dimensional virtual-real fusion experiment system, and the system comprises: an object tracking device which is used for recognizing the type of a real experiment object and positioning the three-dimensional space coordinates of the real experiment object in real time, and obtaining the type of the real experiment object and the corresponding real-time three-dimensional space coordinates; a hand tracking device which is used for identifying the joint points of the operating hand and positioning the three-dimensional space coordinates of the joint points of the operating hand in real time to obtain the real-time three-dimensional space coordinates of the joint points of the operating hand; and a virtual-real fusion processing device which is used for generating a corresponding virtual-real fusion image according to the type of the real experimental object, the corresponding real-time three-dimensional space coordinates of the real experimental object andthe real-time three-dimensional space coordinates of the operating hand joint points. According to the invention, the advantages of the virtual-real fusion technology are fully utilized, the virtual object and the real experimental object are fused, the real experimental object can be tracked and cultivated in real time while a user naturally moves the real experimental object, the experimental operation ability and learning interest of the user are cultivated, and the experimental risk is reduced.

The invention discloses a remote sensing image fusion quality evaluation method, which comprises the steps of extracting LBP feature statistical histograms, edge histograms and spectral features of sub-blocks in different waveband images as feature vectors, and constructing an original multivariate Gaussian model in a training stage; in the test stage, constructing original multivariate Gaussian models in the test stage, and calculating a full-resolution quality evaluation prediction value according to the two original multivariate Gaussian models constructed during training and testing; calculating spectral similarity and spatial similarity between all waveband images of the multispectral image referred by the test remote sensing fusion image and downsampling waveband images obtained after downsampling operation is carried out on all waveband images of the test remote sensing fusion image, and obtaining a resolution reduction quality evaluation prediction value; and obtaining an objective quality evaluation prediction value. Due to the fact that the obtained feature vector information can well reflect the quality change condition of the remote sensing fusion image, the correlationbetween an objective evaluation result and human eye subjective perception is effectively improved.

The invention discloses a land cover change monitoring method, system and device and a storage medium, and the method comprises the steps: fusing SAR data and multispectral data to acquire obtaining afusion image; performing parameter calculation on a fused image to obtain image parameters; and performing change monitoring through a preset land cover dynamic change monitoring model according to the fused image and the image parameters to obtain a land cover change result. The SAR data and the multispectral data are fused, the fused image machine is subjected to change monitoring through the land cover dynamic change monitoring model, all-weather characteristics and texture characteristics can be obviously combined, spectral characteristics can also be combined, the analysis effect is finer, and the monitoring resolution and the monitoring efficiency are greatly improved. The invention can be widely applied to the field of land monitoring.

The invention relates to a method and a system for image fusion based on Curvelet transformation. The method comprises the following steps: interpolating an original hyper-spectral image to get a new hyper-spectral image; carrying out HIS transformation to get components I, S and H, and stretching the gray of an SAR image to get an SAR* image; carrying out Curvelet transformation, and carrying out fusion according to fusion rules to get a new Curvelet transformation coefficient; and finally, carrying out inverse Curvelet transformation to get a component I*, and carrying out inverse HIS transformation on the component I* and the components S and H to get a fused image. The invention further relates to a system which comprises an image interpolation module, an image processing module, a Curvelet transformation module, and an image fusion module. The spectral distortion of fused images is improved, and the spatial resolution is increased greatly.

The invention discloses an intelligent drawing method for a surface water body based on a wide-view-field high-resolution No.6 satellite image, which comprises the following steps: on the basis of selecting high-quality wide-view-field Gaofen-6 remote sensing image data, obtaining wide-view-field 2m-resolution eight-waveband Gaofen-6 fused image data based on a grid division geometric fine correction and image fusion method; carrying out image multi-scale rapid segmentation based on principal component analysis and object-oriented combination, constructing a feature classification vector on the scale of a segmented object, selecting a water body/non-water body training sample with relatively high spatial-temporal representativeness based on a spatial grid, and constructing an object-oriented deep neural network-based surface water body intelligent mapping model for wide-view-field high-resolution No.6 remote sensing image surface water body automatic drawing is realized. The invention mainly improves the efficiency and precision of geometric fine correction, multi-scale segmentation and surface water body intelligent extraction of the wide-view-field image, and has good application potential in flood monitoring, river and lake supervision and water ecology investigation.

The present invention relates to an image fusion method based on guidance filtering. The method comprises the steps of (1) carrying out maximum value filtering on an original infrared image I to obtain an image I<max>, (2) using a guidance filtering algorithm to carry out further filtering processing on the I<max> and output an image I<GF>, (3) directly overlaying the image I<GF> to an original visible light image I<v> and obtaining an image I<f>, (4) carrying out gamma correction on the image I<f> and obtaining a final fusion image. The method is based on pixel fusion, the gray level of only a part of the region is raised, a fusion effect 'unnatural' phenomenon is avoided, in addition the algorithm calculation complexity is low, and the real-time processing is easy.