105 results about "Image intensification" patented technology

A fusion night vision system having image intensification and thermal imaging capabilities includes an edge detection filter circuit to aid in acquiring and identifying targets. An outline of the thermal image is generated and combined with the image intensification image without obscuration of the image intensification image. The fusion night vision system may also include a parallax compensation circuit to overcome parallax problems as a result of the image intensification channel being spaced from the thermal channel. The fusion night vision system may also include a control circuit configured to maintain a perceived brightness through an eyepiece over a mix of image intensification information and thermal information. The fusion night vision system may incorporate a targeting mode that allows an operator to acquire a target without having the scene saturated by a laser pointer. The night vision system may also include a detector, an image combiner for forming a fused image from the detector and a display, and a camera aligned with image combiner for recording scene information processed by the first detector.

A dual camera system uses a single lens and no moving parts in the optical path. A single lens directs a desired scene to a partially reflective beamsplitting mirror, thence to a pair of image sensors. For day / night operation, one sensor may be optimized for nighttime sensitivity, while the other is optimized for daytime operation. Alternatively, one optical path may have greater magnification, thus allowing electronic zooming with no resolution loss. The system may be enhanced with an image intensifier for nighttime operation; digital sensors for improved resolution, or orthogonal gyroscopic accelerometers for image stabilization. Additionally, the monochrome and color images may be produced simultaneously and ‘fused’ algorithmically, yielding an improvement in sensitivity and color resolution compared with a conventional color camera.

Disclosed is an intelligent detecting method for cracks of concrete structures. The method includes the steps: (a), calibrating a camera; (b), intercepting a single crack in a zone to be processed of an obtained image; (c), subjecting the single crack image to image intensification and threshold segmentation binaryzation operation; (d), connecting and filling binary images obtained in step (c) for extracting a target zone contour, and obtaining a crack zone after pruning; (e), obtaining an actual width value of the crack based on a unit pixel length a on the image plane obtained in step (a); (f), drawing a crack skeleton, locating a starting point and a stopping point of the crack, and obtaining the crack length; obtaining the crack starting point position based on the crack starting point position and the initial boundary position of the single crack image zone obtained in step (b); (g), repeating the operation in steps (b), (c) and (d), and obtaining widths, lengths and crack starting point positions of adjacent cracks to obtain the distance between two adjacent cracks.

A method for identifying objects based on affine invariant moments of key points comprises the steps as follows: (1) image pretreatment: processing primitive collecting images so as to reduce influence of invalid information such as noises on the following process, enhance useful information and improve the image intensification; (2) main contour extraction: extracting the main contour to object edge images so as to obtain edge images of an outer contour and filter an interior contour and useless contour branches; (3) key frame extraction: calculating a mass center of the target at first based on target gray images dissected, then extending a plurality of rays to the periphery taking the mass center as an extension point so as to seek for the closest gray extreme point in each ray direction and take all the gray extreme points as the key point assembly; (4) invariable characteristic extraction of affine: extracting affine invariable characteristics of the main contour of the objects and calculating of multistage affine invariable moment vector; (5) object identification: identifying the objects through the characteristic proposed in the step (4) and outputting recognition result.

The invention discloses a deep image intensification method fused with RGB image information. The deep image intensification method comprises the steps of S1, obtaining a mapping relation of coordinates of a deep image and an RGB image; S2, preprocessing the deep image and the RGB image separately; extracting an invalid area in the deep image and labeling the pixel position of the invalid area; conducting edge detection on the RGB image, extracting edge information, and comparing the edge information with the preprocessed deep image to determine an effective supporting edge used for repairingthe deep image; S3, estimating the deep information of the invalid area; along a boundary of the invalid area in the deep image, using information of the effective supporting edge to conduct depth calculation layer by layer from outside to inside; S4, conducting filtering and noise reduction optimizing on a miniature isolation area from which the effective supporting edge cannot be extracted in the deep image to improve the precision of the deep image. By means of the deep image intensification method fused with the RGB image information, the invalid area in the deep image can be repaired on the premise of guaranteeing that the edge is clear, and the sharpening degrees of edges of all deep missing areas can be improved.

A digital amblyopia image aid system provided with individually adjustable function, the structure thereof at least consists of an original image input, a system processing center and an image output after adjusting, wherein the characteristics is the said system processing center can adjust on vision according to individual amblyopia patient, and simulate the image seen by the vision disabled through linear and non-linear method to determine and adjust the suitable image contrast intensification parameters according to users' image intensification effect requirement to enhance the image contrast information and provide optimal image effect, and the said system can be applied in electronic magnification system, added with internet function, making it become class instruction reading system for the vision disabled, while the digital signal processor (DSP) is used for outdoors and convenience increasing, which adds exclusive image contrast intensification function into portable aid to bring more convenience and practicability for the vision disabled.

The invention discloses a low-light video image reinforcing method. The method includes the following steps that video image intra-frame reinforcement is achieved; a frame image of a video undergoes intra-frame reinforcement, the reinforcement parameters are calculated and image reconstruction is carried out according to reinforced image information; video image inter-frame reinforcement is achieved; the frame image serves as a standard frame image; features of a next frame image of the standard frame image are compared with features of the standard frame image, whether the standard frame image needs to be reconstructed or not is judged and intra-frame reinforcement is carried out on the next frame image according to the reinforcement parameters of the determined standard frame image. The method is carried out until the whole video image is reinforced. Gray stretch is carried out on the low-light part of the image, noise will not be reinforced in the reinforcement process and basic information of image gray is maintained.

The invention discloses a method for splicing and homogenizing road face images collected by double-linear-array cameras under the linear laser illumination condition. Two linear-scanning cameras are mounted on the top of the tail of a vehicle, a linear laser is used for illumination, a mileage sensor is used for triggering the cameras to shoot road face images, images obtained by the two cameras are spliced to form one image, lane line and running mark detection is carried out on the image, image gamma correction, namely homogenizing, is carried out on non-lane-line and non-running-mark areas in the image, and image intensification is carried out finally. The real time performance of the method can meet the requirement for real-time collection and real-time homogenizing, interference on image homogenizing of lane lines and running marks can be omitted in the homogenizing effect, and the robustness can be adaptive to images of various road faces like expressways, provincial level roads and municipal roads.

The invention discloses a self-adaptive low-light level image intensification method for reducing color cast, relates to low-light level image intensification methods, and aims to solve the problems that the image color cast is intensified when a conventional low-light level image intensification method is used, and a relatively bright area of an image is over-inhibited or over-intensified when being not well processed. The self-adaptive low-light level image intensification method comprises the following steps: firstly, converting a low-light level image into a RGB (Red, Green, Blue) color space, performing inverted S-shaped conversion, performing inversion, calculating minimum values of different pixel points of reversed images at three RGB channels so as to obtain initial dark channel images, and performing median filtering so as to obtain atmosphere light intensity estimation values; converting the inversion images into an HSV color space, and calculating self-adaptive intensification parameters by taking average gray level values of a V channel as average brightness; calculating transmissivity images according to atmosphere imaging equations, modifying so as to obtain transmissivity smooth images, with the atmosphere imaging equations, performing demisting operation on the three RGB channels of the inversion images, performing inversion, and performing S-shaped conversion, thereby obtaining finally intensified images. The self-adaptive low-light level image intensification method is applicable to intensification processing on images.

The invention discloses a retinal vessel image segmentation method based on deep learning, and the method comprises the steps: carrying out the enhancement of a fundus image, amplifying the data of atraining set, constructing a dense connection convolution block, replacing a conventional convolution block with the dense connection convolution block, achieving the feature reuse, and improving thefeature extraction capability; constructing an attention mechanism module, and performing adaptive adjustment on the feature map to highlight important features so as to suppress invalid features; building a model, building a DA-Unet network, using the processed data set to perform training and parameter adjustment, and obtaining and storing an optimal segmentation model; and carrying out actual segmentation, segmenting the eye fundus image needing retinal vessel segmentation into 48 * 48 sub-block images by using a sliding window, inputting the 48 * 48 sub-block images into a DA-Uet network for segmentation, outputting segmented sub-block image results, and splicing the segmented small block images into a complete retinal vessel segmentation image. The blood vessel segmentation method canautomatically segment blood vessels and has a good segmentation effect on tiny blood vessels.

The invention discloses a digital image fuzzy intensification method for inosculating a plurality of fuzzy operators. The method is divided into four steps: step one, brightness-darkness grades are defined as four grades; step two, for a first grade image and a second grade image , a linear transit function is adopted to make conversion, for a third grade image and a fourth grade image, a nonlinear transit function is adopted to make conversion; step 3, on a fuzzy field, different brightness-darkness grades choose different fuzzy operators to make fuzzy image intensification: for the first grade image, a multinomial intensification operator is adopted to make fuzzy field intensification; for the second grade image, a logarithm type intensification operator is adopted to make fuzzy field intensification; for the third grade image and the fourth grade image, a sectional transit type intensification operator is adopted to make fuzzy field intensification; step 4, inverting back to null-field, fuzzy intensified null-field images are obtained and sent out. The invention can get a better visual efficiency whether the image is of insufficient exposure or over exposure.

Using CCD camera, image intensifier, reflecting mirror with large caliber and laser projector, Remote all-weather automatic intelligent monitoring instrument is applicable to environment under normal illumination, low illumination even 0 LUX illumination. The instrument is composed of hardware system and software system. The hardware includes reflector, day and night switching system, daylight image system, night image system and laser illumination system. The software includes control module, image process module, correction module for atmospheric disturbance, and module for correcting and capturing images. The invention is capable of recognizing persons at 10 km under normal illumination, or at 1-4 km under 0 LUX, or at 6 km under 0.00 LUX. The instrument possesses functions of zooming target, automatic switching day and night, image capture and recording. The invention is applicable to frontier defense, public security for monitoring important area and searching object etc.

An image intensifying device includes a lens that is positioned at a light input that forms an image of a scene. The image intensifying device also includes an image intensifier tube that includes a photocathode that is positioned to receive the image formed by the lens. The photocathode generates photoelectrons in response to the light image of the scene. The image intensifier tube also includes a microchannel plate having an input surface comprising the photocathode. The microchannel plate receives the photoelectrons generated by the photocathode and generating secondary electrons. An electron detector receives the secondary electrons generated by the microchannel plate and generates an intensified image of the scene.

The invention relates to a visual positioning method using an apple fruitlet bagging robot, in particular to an apple fruitlet bagging robot visual positioning system based on machine vision. The method is capable of carrying out accurate image processing and analysis on apple fruitlets, positioning the apple fruitlets accurately and fast through algorithms, determining accurate positions of the fruitlets, and guiding a mechanical arm to accomplish the bagging work. The method comprises a hardware part and a software part, wherein the hardware part comprises an image collecting device, a storage device, a processor and a controller, and the software part comprises an image processing program and a positioning program. The apple fruitlet bagging robot visual positioning method is achieved by ROI extraction, graying, image intensification, image segmentation, camera calibration, image epipolar line calibration, image feature extraction and matching, and image three-dimensional reconstruction.

The invention relates to a cotton foreign fiber image online segmentation method and a system on the condition of wind power delivery. The method includes procedures: S1 cotton foreign fiber images which are collected timely are read; S2 the color space of the cotton foreign fiber images is converted into hue saturation value (HSV) color space from red-green-blue (RGB) color space; S3 saturation (S) component of the cotton foreign fiber images with the HSV color space, and image intensification process of the S component images is conducted; S4 fixed threshold binaryzation image segmentation of the S component intensification images is conducted to obtain primary segmentation images, and denoising process of the primary segmentation images is conducted through an area threshold method; and S5 a morphology method is used for connection of breakage gaps existing on the denoised images. On the condition that the segmentation quality of the images is ensured, the method and the system can effectively improve the speed of image segmentation.

The invention discloses an X-ray detection compartment comprising a compartment body and an X-ray photographing system arranged in the compartment body. The X-ray photographing system comprises an X-ray tube, an image intensifier, a high-pressure generator, an image processing system and a control system. The X-ray photographing system is connected with the compartment body through a detachable fixing device so as to be improved into a movable structure. The X-ray photographing system can work at any position in the compartment through demounting of the fixing device and can be used outside the compartment as needed, so that the X-ray detection compartment is high in working flexibility and outstanding in working capability.

This invention relates to a low light imaging sensors and particularly image intensification and CMOS sensors. To overcome issues of dazzle and halo when operating in areas where the scene encompasses bright light sources, the invention provides material layers in contact with the detector material to spatially limit the generation or subsequent diffusion of electrons in said detector material. This allows the imaging sensor to perform as normal under bright conditions, maintaining the operator's scene awareness and spatial acuity.

The invention discloses an early tobacco virus disease detection method based on an infrared thermal imaging technology, which comprises the steps that a tobacco infrared thermal image is subjected to image intensification from image smoothing, image sharpening and histogram equalization; a new tobacco leaf thermal image area and an old tobacco leaf thermal image area are extracted by a threshold segmentation method; if a temperature of a growth environment of tobacco is higher than 16 DEG C and less than or equal to 28 DEG C, a difference value between temperature values of various pixel points in the new tobacco leaf area and an average value of the temperature values is calculated; when an absolute value of the difference value is greater than 1 DEG C, a plant is a disease plant; if not, the plant is not the disease plant; when the environment temperature is higher than 28 DEG C and less than 34 DEG C, a difference value between average temperatures of the pixels in the new tobacco leaf area and the old tobacco leaf area is calculated; when the difference value is less than 0.8 DEG C, the plant is the disease plant; and if not, the plant is not the disease plant. The method is simple in step, short in consumed time and high in accuracy, can detect a disease earlier, can be applied to long-distance real-time monitoring of a field crop, and provides timely and reliable information of a crop growth condition, a disease condition and the like.

A target-specific image-enhancing agent for medical imaging comprises an extended poly(amino acid), wherein at least 90 percent of the amino acid residues are conjugated to signal-generating moieties attached to signal-controlling moieties via bonds that are cleavable by a physiological substance produced by the target. The image-enhancing agent becomes activated when the bonds is cleaved by the physiological substance. The image-enhancing agent is used in detecting and / or diagnosing a disease that is characterized by an overproduction of the substance.

An optical system includes an optical bench defining a mounting surface, an objective lens mounted to the optical bench, and an image intensifier tube. The image intensifier tube includes: (i) a housing defining an interior region and a mounting surface, and (ii) a photocathode bonded to a glass faceplate defining a faceplate surface, wherein the photocathode is positioned within the interior region of the housing, and wherein the mounting surface of the housing resides on the same plane as the faceplate surface. The mounting surface of the optical bench is mounted onto the mounting surface of the image intensifier tube housing such that the mounting surface of the optical bench resides on the same plane as the faceplate surface and the faceplate surface of the faceplate is the surface of the faceplate that is positioned closest to the objective lens.

An objective lens assembly for use in an image intensifier over the 0.6 to 0.9 micron spectral bandwidth resulting in a F# within a range of 1.0 to 1.5 and a field of view within the range of 35 to 45 degrees. The assembly includes an air-spaced doublet, a right angle fold prism for bending the optical axis 90 degrees to a reoriented optical axis, two lens subassemblies, and a field flattening lens.

A method of spectrographic measurement is disclosed. The method generates light energy using a solid state low capacitance excitation source, the light energy being caused to fall on a sample to be assayed, causing the sample to output an output optical signal. The method generates a plurality of modulation frequencies, and a plurality of heterodyne frequencies to form a set of heterodyne signals at the heterodyne frequencies. Each of the heterodyne frequencies is associated with one of the modulation frequencies. Coupling the modulation frequencies to the excitation source, causes the excitation source to generate excitation energy modulated in intensity in proportion to the modulation frequencies. A sampling a portion of the substantially incoherent excitation energy forms a reference substantially incoherent excitation signal. Focusing the output optical signal as an image modulated with the plurality of modulation frequencies on an image intensifier enables the formation of an intensified image modulated with the plurality of modulation frequencies, receiving the intensified image modulated with the plurality of modulation frequencies on a multielement optical detector.

An image fusion method based on contrast analysis comprises the following steps of: 1, constructing the morphological gradient graph of an input image and the morphological gradient graph of an enhanced image of the input image separately; 2, subjecting the morphological gradient graphs of the input image and the enhanced image thereof to subtraction to obtain a contrast difference graph of the unenhanced and enhanced input images; 3, creating a fusion coefficient matrix according to the contrast difference graph; and 4, fusing the input image and the enhanced image thereof according to the fusion coefficient matrix, subjecting fused processed image pixels to range control in order that the pixels are within a reasonable image range and are output. The single-graph fusion of the present invention aims at the fusion of the input image and the enhanced image thereof which have the same image information essence and is not fusion of different images. The image output by the image fusion method has uniform grayscale distribution and has completely retained details. The method is effective on not only grayscale images, but also color images, and is higher in fidelity of the restoration of the color images in associated color channels.

An image morphing method is suitable for generating an intermediate image sequence. First, a control point CP={(pi,qi)}i=1 . . . N is specified and marked in a source image Is({right arrow over (x)}) and a destination image Id({right arrow over (x)}′). Next, an edge gradient parameter (Ise({right arrow over (x)}), Ide({right arrow over (x)}′) is computed according to the source image Is({right arrow over (x)}) and the destination image Id({right arrow over (x)}′). Next, a total objective function E(Df,Db) is computed according to the above-mentioned control point CP and edge gradient parameter (Ise({right arrow over (x)}), Ide({right arrow over (x)}′)). The above-mentioned intermediate image sequence is generated by using the total objective function E(Df,Db). The present invention utilizes the edge gradients of the source image Is({right arrow over (x)}) and the destination image Id({right arrow over (x)}′) to enhance the constraint of image morphing. Thus, the image morphing effect is promoted.

The invention relates to an image fusion method based on potential low-rank representation nested rolling guide image filtering, and the method comprises the steps: carrying out the decomposition of a to-be-fused infrared image and a to-be-fused visible light image through potential low-rank representation, and obtaining a corresponding low-rank sub-layer and a significant sub-layer; performing multi-scale decomposition on the two low-rank sub-layers, extracting a detail layer, and performing weighted merging to obtain an enhanced layer; fusing the two low-rank sub-layers by using a weighted guided image filtering algorithm based on improved visual saliency mapping to obtain a low-rank sub-layer fused image; fusing the saliency sub-layers by using a pyramid decomposition-based region energy feature adaptive weighted fusion method to obtain a saliency sub-layer fused image; and adding and reconstructing the enhanced image layer, the low-rank sub-layer fusion image and the saliency sub-layer fusion image to obtain a final fusion image. According to the method, rich detail information of the source image can be reserved, the definition and contrast of the fused image are improved, and the fusion performance is good.