191 results about "Image fusion algorithm" patented technology

The invention relates to a behavior splicing method of a video scene, so that video scene splicing and behavior splicing of moving objects in different videos can be realized. The method comprises the following steps that: on the basis of image registration, meshing is carried out on a video image so as to form an image block node set and an image block label set; an image block clustering algorithm is utilized to generate an image block label type representative candidate set for each of the image block node; and a two dimensional Markov random field and a three dimensional Markov random fields of the image block node are constructed and solved, so that a static background and a dynamic background are generated; on the basis of extraction of tracks of moving objects, track similarity and position similarity between moving objects of different videos are calculated, so that a splicable property of moving object behaviors is obtained and video alignment is provided at time and space; and a poisson image fusion algorithm is utilized to fuse a panoramic dynamic background video and motions, so that a video scene after behavior splicing is generated.

The invention provides a multi-scale gradient domain image fusion algorithm which is used for fusing infrared thermal images with visible light images. The multi-scale gradient domain image fusion algorithm is characterized by comprising the following steps: a step of constructing gradient fields; a step of correcting gradient fusion weight; a step of reconstructing gradient fields; and a step of fusing images through a transparency method. The multi-scale gradient domain image fusion algorithm provided by the invention can be used for simultaneously reserving temperature rise areas and detail information of infrared thermal images and visible light images, removing a halation background-effect phenomenon and redundant information due to interaction among images and avoiding color inconsistency of an infrared thermal image temperature rise area.

The invention discloses a novel image fusion algorithm based on combination of IHS (intensity-hue-saturation) transform and wavelet transform for effectively fusing a low-resolution multispectral image and a high-resolution full-color image. The method comprises the following steps: carrying out wavelet decomposition on a strength (I) component image and a high-resolution full-color image of a multispectral image; carrying out fusion with different standards according to respectively high and low frequency features; carrying out wavelet inverse transformation on the new formed high and low frequencies to obtain a new I component image; and finally carrying out IHS inverse transformation to obtain a final fusion image. A test result shows that the method is used for not only reducing the spectral distortion value, but also effectively increasing the spatial resolution.

The invention discloses a venous visualizer and a visualizing method. The visualizer comprises an optical system for visualizing deep veins, and a liquid crystal display system, wherein the optical system comprises a near infrared light source and a venous image acquisition unit; the venous image acquisition unit is used for dividing reflected light into two paths so as to separate near infrared light from visible light; the near infrared light comprises information of deep veins (3 millimeters); after being subjected to image enhancement, the near infrared light is subjected to image fusion with a visible light image, and a fused image is displayed on the liquid crystal display screen for a doctor to refer to during venipuncture. Due to the adoption of a dynamic frame-by-frame image fusion algorithm, a venous enhanced image obtained by near infrared light activation can be overlapped on an ordinary image in real time, and an entire venipuncture process can be displayed completely on the liquid crystal screen.

The invention relates to a partial discharge detection method based on multi-source image fusion. The partial discharge detection method based on multi-source image fusion is characterized in that S100, an ultrasonic sensor array, a camera, an infrared thermal imager, and an ultraviolet imager are respectively used to acquire ultrasonic wave signals, visible images, infrared images, and ultraviolet images of a detected device synchronously; S 200, the ultrasonic visible images can be acquired based on the acquired ultrasonic wave signals and the visible images; S 300, the ultrasonic visible images, the infrared images, the ultraviolet images can be used as the partial discharge detection result after being fused by adopting the image fusion algorithm, and can be displayed. Compared to the prior art, the partial discharge detection method has advantages of detection precision and high reliability.

The invention discloses an infrared and low light image fusion method. The method comprises the following steps of: (1) acquiring an original infrared image by using an infrared camera, and acquiring an original low light image by using a low light camera; (2) respectively partitioning the acquired original infrared image and the original low light image so as to divide the original infrared image and the original low light image into N mutually corresponding original image sub-blocks; (3) in a parallel processing system, simultaneously carrying out image fusion on the mutually corresponding original image sub-blocks in the original infrared image and the original low light image so as to obtain fused image sub-blocks; and (4) combining the fused image sub-blocks so as to generate a final fused image. The fused image obtained by the invention not only has a high image quality, but also has a high fusion speed, and therefore, the infrared and low light image fusion method has a wide application prospect in the military and civil fields of night vision surveillance, security monitoring, and the like.

The detection equipment includes detection system and electric control system. The detection system includes case, separating machine, input port, light source, video camera, container conveying belt, cleaning unit, residual liquid detecting module, container side detecting module, photoelectronic sensor, inferior container eliminator and output port. The electric control system includes host control PC and detection subsystems, the detection subsystems consist of photoelectronic sensor, image collector, DSP processing module and I/O interface; and the host control PC is connected electrically via I/O interface to the detection subsystems. The present invention adopts infrared and visible image fusion algorithm and polyfocusing image fusion algorithm in the detection. The present invention has the beneficial effects of fast detection speed and high precision.

The embodiment of the invention discloses a video image processing method comprising the following steps of: obtaining one frame video image; extracting and segmenting a foreground image and a background image from the video image; optimizing the foreground image; carrying out processing of multi-frame accumulation algorithm on the background image; and combining the processed foreground image and background image into one image through an image fusion algorithm. According to the video image processing method provided by the invention, the foreground image and the background image are respectively processed to ensure that the image quality can be overall increased, the image layering is enhanced and the contour of an object in the background can be clear. In addition, the invention also provides a video image processing device.

The present invention discloses a hybrid multi-scale analysis-based infrared and visible light image fusion algorithm. The algorithm comprises the following steps that: step 1, NSCT (nonsubsampled contourlet transform) decomposition is performed on infrared and visible images to obtain low-frequency subbands and high-frequency subbands; step 2, static wavelet transformation is performed on the low-frequency subbands so as to obtain a low-frequency subband and three high-frequency subbands, and a local energy and maximum absolute value selection-combined method and a compressed sensing theory are adopted to fuse the low-frequency subband and the three high-frequency subbands; step 3, the definition of an image to be fused is judged, and the enhancement layers of an LSCN are selected according to judging criteria; step 4, the topmost layer high-frequency subband is fused according to a fusion rule of selecting a largest absolute value, and the other subbands are fused through adopting an improved PCNN model; and step 5, inverse NSCT is performed on fusion results, so that a final fused image is obtained. The fused image obtained by using the method of the invention has prominent edges and high contrast; a target in the fused image is prominent; and the indexes of the algorithm such as average gradient and spatial frequency are higher than those of algorithms in the prior art.

The invention discloses infrared and visible light image fusion method based on ADC-SCM and low-rank matrix expression, relating to the technical field of image processing.. The fusion method combinesadaptive dual channel pulsed cortex (ADC-SCM) and low rank matrix (LRR) theory to propose an effective infrared and visible image fusion algorithm. According to the fusion method, an effective infrared and visible light image fusion algorithm is provided by combining an adaptive double-channel pulse distribution cortex (ADC-SCM) and a low-rank matrix (LRR) theory; The method comprises the following steps: firstly, combining low-rank expression with a frequency modulation (FT) saliency algorithm to carry out salient region detection on an infrared source image, thereby separating a salient region from a background region in the source image; Secondly, fusing the two obtained regions respectively, and selecting a fusion rule with the maximum absolute value to fuse the significant regions inorder to retain the significant features to the maximum extent; And finally, obtaining a fused background through NSST inverse transformation, and superposing the fused salient region and the fused background region to obtain a final fused image. Experimental results show that the algorithm provided by the invention is superior to other common image fusion algorithms in subjective visual effect and objective evaluation indexes.

The invention provides a multi-focus image fusion method based on sparse decomposition and a differential image. The multi-focus image fusion method is characterized by comprising the steps of utilizing RPCA to decompose an original image to obtain its sparse component and low-rank component, enhancing an edge of the sparse component through guide filtration, adding the enhanced sparse component to the low-rank component to construct an enhanced image comprising a strong edge, performing differential processing on the enhanced image and the original image, calculating spatial frequency of an obtained differential image, utilizing a self-adaptive threshold algorithm to obtain a spatial frequency diagram of the differential image, utilizing a morphological algorithm to remove a pseudo-focus region in the spatial frequency diagram to obtain a fusion decision-making diagram, and reconstructing a fusion image according to the fusion decision-making diagram and a fusion rule. Three index of the multi-focus image fusion method in evaluating an image fusion algorithm, namely a correlation coefficient, edge gradient information and an edge correlation factor are higher than that of other classical algorithms by more than 45%, 6% and 15% respectively.

The invention relates to a camera interchangeable dynamic light splitting imaging system and a method thereof applied to high-dynamic imaging. The system realizes high-quality imaging of a target by adopting a combined imaging mode, combining a rear-end image fusion processing algorithm, and forming a structure of a main lens, an adaptor, a plurality of cameras and multi-source image fusion. According to the system, a single-sensor imaging mode of a one-time single light path of a conventional optical measuring system is changed into a dynamic light splitting multi-sensor imaging mode; key technologies, such as a large dynamic range imaging scheme, a light splitting ratio dynamic adjustable adaptor module and a multi-wave band high-dynamic image fusion algorithm, are broken through; the problems about integrated installing and synchronous imaging of a plurality of types of advanced sensors, multi-source image fusion, an enhanced processing algorithm and the like are solved; optical measurement information are acquired to the maximal extent; high-dynamic, high-speed, multi-wave band, high-resolution and high-quality imaging of a target in a target range is realized; the optical measurement level of a test in the target range is effectively improved.

The invention discloses a wireless sensor network image fusion method based on multi-focus fusion and image splicing. According to the wireless sensor network image fusion method, multi-focus image acquisition is firstly carried out on an image acquisition node, a multi-focus image fusion algorithm based on direction area energy is called to perform multi-focus image fusion, a focused image is selected by an adaptive gradient threshold judging method and is transmitted to a splicing processing node, multi-focus images obtained from different directions in different acquisition nodes or the same acquisition node are spliced by the splicing processing node, and a multi-azimuth and multi-focus fusion image is obtained and is then transmitted. The invention combines the multi-focus image fusion technology with the image splicing technology, effectively fuses a large number of image data to be transmitted, so the data transmission quantity and transmission energy consumption of the sensor network are reduced, meanwhile, users are ensured to obtain more direct and more abundant information, the adaptive gradient threshold judging method is adopted to select splicing materials, and the adverse effect of focusing blurred images on fusion result is reduced.

The invention discloses a mixed-reality sandbox device and method. The sandbox device is formed by a prime module sandbox, a projector and a computer. The input is from a plurality of monitoring cameras in the real world, which collect local scenes of the real world and provide input video streams for the device; the prime module sandbox of the device is made according to the real world in a certain scale; the surfaces of regions corresponding to the input video streams are light-color diffuse reflection materials; and the projector is arranged above the prime module sandbox, and projects downwards onto the surface of the sandbox. The method adopts a two-step alignment strategy to calibrate the input video streams, then, carries out splicing on overlap regions through an edge-adaptive image fusion algorithm, and enables images and videos in the real scene to be fused and projected to the surface of the prime module sandbox in real time in the device operation process according to the obtained calibration result, thereby realizing real-time simulation and miniature reproduction of dynamic change of the real scene.

The invention relates to the digital image processing field and the image fusion algorithm field, for greatly improving the running speed of the whole fusion system on the premise that the quality and detail of the image are not impacted, the data volume of the image is greatly reduced and the amount of the data to be processed for fusion process is promoted. Therefore, according to the technical scheme, the image fusion method of compressed sensing framework comprises following steps: step 1, executing the single layer wavelet decomposition on the image to be fused; step 2, selecting the Gaussian along matrix as the measure matrix, measuring and obtaining the value; step 3, selecting the numerical value corresponding to each point for forming the new matrix; step 4, adopting the principal component analysis method for fusing the high-frequency components of the source images after measured; step 4, obtaining the high-frequency component, which is suitable for the wavelet inverse transform; and step 5, obtaining the fused image. The image fusion method of compressed sensing framework is mainly used for the digital image processing field.

The invention discloses and provides an effective multi-focus image fusion method based on the transformation between Pulse Coupled Neural Vetwork (PCNN) and Laplacian Pyramid (LP). Firstly, the LP is utilized to perform multi-scale decomposition to images with a tower structure and the PCNN is utilized to process the decomposed images of each of the scales so as to acquire a neurons firing frequency diagram of a describing feature cluster; the fusion to the images at each of LP decomposition scales is realized, based on the local space frequency (LSF) of the firing frequency diagram; and lastly, the fusion to the multi-focus images are realized through restricting algorithm of LP decomposition. The experimental results show that the multi-focus image fusion result acquired by the method of the invention has advantages over various traditional fusion algorithms at the aspects of subjective visual effect and objective evaluation index; and the method shows good performance.

The invention discloses a three-camera module and electronic equipment. The three-camera module comprises a first color lens, a second color lens, a black-white lens, a first triple prism, a second triple prism, a first color photosensitive chip, a second color photosensitive chip, a black-white photosensitive chip and an image processor; the first triple prism is used for reflecting the beam penetrating the first color lens to the first color photosensitive chip to perform the imaging; the second triple prism is used for reflecting the beam penetrating the second color lens to the second color photosensitive chip to perform the imaging; the black-white photosensitive chip and the black-white lens are opposite to each other so that the beam penetrating the black-white lens can directly enter the black-white photosensitive chip to perform the imaging; the image sensor acquires the image information on each of the first color photosensitive chip, the second color photosensitive chip andthe black-white photosensitive chip; and a target image after the image fusion is output through an image fusion algorithm. The three-camera module realizes a multifunctional camera module.

The present invention discloses a component level three dimensional model building method of bayesian network constraint. The method comprises the following steps of firstly, placing a target object on a turntable rotating slowly, using a Kinect to scan the target object to obtain a RGBD data sequence, and generating an initial three dimensional point cloud by combining a depth image fusion algorithm (KinectFusion); secondly, utilizing an interactive segmenting tool to modify the initial three dimensional point cloud to remove a noisy point and the redundant part; on the basis, utilizing a trained bayesian network to carry out the component segment on the three dimensional point cloud, automatically selecting a component model according with the requirement from a three dimensional model component base for each segmented component, registering a candidate three dimensional component to the model three dimensional point cloud by utilizing a non-rigid deformation algorithm, calculating the fitting degree of the candidate three dimensional component, and further selecting an optimal three dimensional component; finally, automatically splicing the optimal three dimensional component by utilizing a conformal deformation algorithm, and fitting to the model three dimensional point cloud in a deformation manner to obtain the three dimensional model possessing the component semantics finally.

The invention provides a substation equipment defect recognition method and device, and particularly relates to the technical field of data analysis and processing, and the method can comprise the steps: building an infrared and visible light double-vision system, and obtaining a visible light image and an infrared image of detected substation equipment; carrying out the fusion through employing an image fusion algorithm of IHS transformation and Contourlet transformation, and generating a to-be-recognized image after fusion; constructing a neural network, performing feature extraction on the batch of original video data, and establishing an equipment defect database including abnormal data of the power transformation equipment in different states; then constructing a transfer learning network based on infrared image features to obtain a temperature mode analysis result of the equipment area; and finally, carrying out anomaly identification and defect judgment on the to-be-recognized image by utilizing a multi-network fusion algorithm. Based on the scheme provided by the invention, the intelligent degree of substation equipment fault detection is improved, and the efficiency and accuracy of fault detection are improved.

The invention relates to a fast multi-focus image fusion algorithm. The algorithm comprises the following steps of firstly, decomposing a source image by using a smoothing filter and constructing thedecision graph model of multi-focus image fusion; secondly, using the correlation between the neighborhood pixels of multi-focus images to provide a fast steering filter optimization decision graph sothat calculating complexity is greatly reduced; then, reconstructing two scales of images; and finally, using the fusion method of the invention and other triple existing fusion methods to fuse the multi-focus images. Compared with the other three kinds of fusion methods, by using the method of the invention, regardless of subjective visual and objective evaluation indexes, the texture information, the significance information and other details of the multi-focus images can be preserved.

The invention provides a contrast pyramid image fusion method based on an area. The method is characterized by utilizing a plurality of out-of-focus images to obtain a sharp image. The method is characterized by, to begin with, utilizing Laplace energy in an image window as measurement for showing whether the area is clear or not; obtaining positions of clear pixel points in each image; marking the positions of the clear pixel points in a mask image; then carrying out optimization operation on the mask image to obtain an accurate position of the clear area in the image; fusing each layer of image of a contrast pyramid with a ground floor image according to the marked clear area in a mask image pyramid; and establishing a completely clear image by utilizing the fused contrast pyramid and the ground floor image. The multi-focus image fusion algorithm can be applied to the processing of criminal investigation images, medical microscopic images, and does not rely on any camera parameters.

The invention discloses a rapid image fusion algorithm based on the pixel level. The method comprises steps that firstly, color space transformation of a visible light image is carried out, and the visible light image is transformed from the RGB space to the YUV space; secondly, adaptive weights of the visible light image and an infrared image are acquired respectively; thirdly, the weighted meanmethod is utilized to process the fusion process of the visible light image and the infrared image; fourthly, the fusion result is optimized and adjusted; and lastly, color space transformation of thevisible light image is carried out, the image is transformed from the YUV color space to the RGB space, and the whole image fusion process is completed. The method is advantaged in that the visible light image and the infrared image can be fused on the pixel level, the abundant spectrum information and the high resolution of the visible light image and the unique heat radiation characteristic ofthe infrared image are fully integrated, and maximized image information fusion is realized.

The present invention discloses an adaptive image fusion method based on chromaticity coordinates. The realization processes of the method comprise firstly obtaining the chromaticity coordinate mean values of a foreground image and a background image; secondly, modifying the R, G and B values of each pixel in the foreground image according to the chromaticity coordinate mean values of the foreground image and the background image; then utilizing a Gauss fuzzy algorithm to obtain the weighting coefficients of the edges of the foreground image, and carrying out the gradient weighted fusion on the foreground image and the background image at the foreground edges. According to the algorithm given out by the present invention, the brightness and the chromaticity of the foreground image can be adjusted adaptively according to the background image, on the condition that the foreground and background colors and brightness have greater difference, the situations that the synthetic images are clear, and the foreground color and brightness do not distort, can be kept, and the situation that the edge synthesis of the foreground image is excessively natural can be guaranteed by utilizing a Gauss fuzzy method to obtain the weighting coefficients of the edges of the foreground image and utilizing the gradient weighted fusion processing. Relative to a Poisson image fusion algorithm, the method of the present invention has a lower calculation complexity and can be widely used in some mobile terminal programs of limited hardware resources.