795 results about "Low contrast" patented technology

The teachings as provided herein relate to a watermark embedded in an image that has the property of being relatively indecipherable under normal light, and yet decipherable under UV light. This fluorescent mark comprises a substrate containing optical brightening agents, and a first colorant mixture pattern printed as an image upon the substrate. The colorant mixture pattern layer has as characteristics a property of strongly suppressing substrate fluorescence, as well as a property of low contrast under normal illumination against the substrate or a second colorant mixture pattern printed in close spatial proximity to the first colorant mixture pattern. The second colorant mixture pattern having a property of providing a differing level of substrate fluorescence suppression from the first such that the resultant image rendered substrate suitably exposed to an ultra-violet light source, will yield a discernable image evident as a fluorescent mark.

It is possible to estimate optical characteristic according to a pupil diameter in daily life of an examinee, correction data near to the optimal prescription value, eyesight, and sensitivity. A calculation section receives measurement data indicating refractive power distribution of an eye to be examined and pupil data on the eye and calculates lower order and higher order aberrations according to the measurement data and the pupil data (S101 to 105). For example, a pupil edge is detected from the anterior ocular segment image and a pupil diameter is calculated. By using this pupil diameter, lower order and higher order aberrations are calculated. According to the lower order and higher order aberrations obtained, the calculation section performs simulation of a retina image by using high contrast or low contrast target and estimates the eyesight by comparing the result to a template and/or obtains sensitivity (S107). Alternatively, according to the lower order and the higher order aberraations obtained, the calculation section calculates an evaluation parameter indicating the quality of visibility by the eye to be examined such as the Strehl ratio, the phase shift (PTF), and the visibility by comparison of the retina image simulation with the template. According to the evaluation parameter calculated, the calculation section changes the lower order aberration amount so as to calculate appropriate correction data for the eye to be examined (S107). The calculation section outputs data such as the eyesight, sensitivity, correction data, and the simulation result to a memory or a display section (S109).

A machine vision inspection system is programmed and operated to identify one or more lines appearing in a highly-textured and/or low-contrast surface of a workpiece. In a learning mode, a line-enhancing image is generated from a captured image of the workpiece. In various embodiments, the enhanced image is based on a previously determined technique governed by a selected value for an associated parameter. A line transform is used to transform the enhanced image. The transformed data is analyzed to identify local extrema corresponding to the lines to be identified. Part program instructions are created to automatically generate the line-enhancing image, to transform it, and to analyze the transformed data set to identify the lines to be detected. Line constraint(s) that characterize a consistent line arrangement are used to improve the speed and reliability of the line detection.

A method of analyzing machine vision images to identify low contrast features such as scratches or cracks on the polished ends of optical fibers. A bank of oriented filters having incremental angles of orientation are tuned to respond to the frequency characteristics of oriented scratches or cracks having an approximate width. The filters are applied to an image to generate a filter response image for each filter orientation. The set of filter response images are combined to form a single data set indicating a magnitude and angle for each pixel in the original image. Elements of the combined data set having corresponding angles and magnitudes are grouped to form contour components that can be optionally input to higher order processes.

The invention provides a method for the outline automatic extraction of a level set medical ultrasonic image area based on an edge and a statistical characteristic, aiming at the characteristics of low contrast degree and small signal-to-noise ratio of medical ultrasonic images and designing a method for the outline extraction of the level set area based on the edge and the statistical characteristic. The method comprises three steps of: 1, carrying out area outline coarse extraction, completing image inverse extraction and self-adapting Gaussian function background suppression, applying otsu image automatic threshold algorithm to convert the image into a binary image, and carrying out image degreasing interference operation and closed area outline extraction work; 2, adopting a selectivity aeolotropy medical ultrasonic image smoothing algorithm to pre-treat an original image; and 3, carrying out the precise extraction of the level set image area based on the edge and the statistical characteristic. Experimental results indicate that compared with the prior method, the method of the invention can obtain a more precise partition result.

The teachings as provided herein relate to a watermark embedded in an image that has the property of being relatively indecipherable under normal light, and yet decipherable under infrared illumination when viewed by a suitable infrared sensitive device. This infrared mark entails, a substrate reflective to infrared radiation, and a first colorant mixture and second colorant mixture printed as an image upon the substrate. The first colorant mixture layer in connection with the substrate has a property of strongly reflecting infrared illumination, as well as a property of low contrast under normal illumination against a second colorant mixture as printed in close spatial proximity to the first colorant mixture pattern, such that the resultant image rendered substrate suitably exposed to an infrared illumination, will yield a discernable image evident as a infrared mark to a suitable infrared sensitive device.

Provided are an image fusion apparatus and method for combining multi-exposure images. The image fusion apparatus and method may generate a sharp high-resolution high dynamic image while fully representing detail in an over-exposed region and an under-exposed region of the image without contrast degradation.

A color gamut that has been mapped from a monitor gamut into a printer gamut by gamut mapping is narrower than the printer gamut. Hence, an image to be output, which is corrected by gamut mapping, has low contrast and poor vividness. Hence, a gamut enlargement correction unit (1208) maps an intermediate mapped gamut into the printer gamut with reference to the intermediate mapped gamut obtained by mapping the monitor gamut, and the printer gamut, thus obtaining a mapped gamut for gamut mapping.

An image processing device captures a normal light image of an observed region via a plurality of color filters having different spectral characteristics, respectively, generates a uniform image having low contrast by extracting each picture signal corresponding to a picture signal in a wavelength band for which light absorption characteristic of a contrast region in the observed region becomes low, and corrects each picture signal of a fluorescence image of the observed region by using the uniform image.

The teachings as provided herein relate to a watermark embedded in an image that has the property of being relatively indecipherable under normal light, and yet decipherable under UV light. This fluorescent mark comprises a substrate containing optical brightening agents, and a first colorant mixture printed as an image upon the substrate. The colorant mixture layer has as characteristics a property of strongly suppressing substrate fluorescence, as well as a property of low contrast under normal illumination against the substrate or a second colorant mixture printed in close spatial proximity to the first colorant mixture, such that the resultant image rendered substrate suitably exposed to an ultra-violet light source, will yield a discernable image evident as a fluorescent mark.

A system and method for creating a combined or mixed-energy image using both low- and high-energy CT data sets acquired using a dual-energy CT system. The low- and high-energy datasets are mixed using desired weighting factors to mimic a “single-energy” image. The low-energy dataset provides data with improved contrast enhancement, but with increased noise level. The high-energy dataset provides data with lower contrast enhancement, but with better noise properties. By combining the low- and high-energy datasets in accordance with the present method, the resulting mixed-energy images utilize the information of full dose of radiation used in the dual-energy scan. A plurality of weighting metrics can be selected, including patient size, dose partitioning, or image quality, to determine the desired weighting factors based on the weighting metrics. By selecting the proper weight factors, image noise can be reduced and/or the contrast to noise ratio can be increased in the mixed-energy image.

The invention provides a computerized tomography (CT) image metal artifact correction method, a computerized tomography (CT) image metal artifact correction device and a computerized tomography (CT) apparatus. The computerized tomography (CT) image metal artifact correction method comprises the following steps that: a metal projection range caused by an interference object is determined according to an original image corresponding to original projection data; diagnosis object projection data after the removal of the interference object are obtained based on metal projection data in the metal projection range, and after that, the original projection data are corrected and a model image is constructed based on the diagnosis object projection data; and secondary correction is performed on the original projection data according to the projection data of the model image, and reconstruction is performed based on corrected target projection data and according to clinically-used scanning and image construction conditions so as to obtain a metal artifact-free target image, and therefore, the purpose of metal artifact correction can be achieved. According to the computerized tomography (CT) image metal artifact correction method of the invention, the original projection data are adopted as a correction object, and therefore, the spatial resolution and low-contrast ability of a processed image can be ensured; and the original projection data completely contain all information of the interference object, and therefore, the introduction of a new artifact can be avoided.

The invention provides a road surface crack detection method based on dynamic programming, comprising the following steps: 11, utilizing a crack with low fuzzy gradient and path tracking contrast ratio for effectively strengthening; 12, utilizing a dynamic programming method to effectively detect the crack with poor continuity; and 13, utilizing a support vector machine model to classify the extracted crack. The road surface crack detection method based on dynamic programming of the invention can effectively extract the crack with low contrast ratio and poor continuity.

In a contrast correcting apparatus 1 which divides an image into unit regions and carries out a contrast correction for each of unit regions, a gray level histogram calculation section 201 generates gray level histograms of the image, and a scene judgment section 202 makes a judgment on the state of the image. When the result of the judgment shows that the image is in a state such as overexposure, underexposure, low contrast or high contrast, a region size determining section 204 sets a greater size as the size of the unit regions. Based upon the size of the unit regions thus determined and the amount of contrast correction determined by the contrast correction amount determining section 203, gray level transformation curves are formed for the respective unit regions, and by using these, the contrast corrections are carried out on the respective unit regions. With this method, it is possible to properly reduce the occurrence of unevenness in gray levels in an image that tends to arise in the case when the size of the unit regions is small although the amount of contrast correction (the amount of emphasis) is great.

The present invention relates to a low-illuminance video image enhancement method and system. The steps of the method include: according to the Weber-Fechner law, performing brightness layering on the input image to obtain a low-contrast area, a De Vries area, a Weber area and a saturated area ;Collect low-illuminance high-definition video images as a training set, and carry out brightness layering on the images in the training set; take the image enhancement quality evaluation function as the objective function, and use an iterative algorithm to adjust the Retinex parameters of the three image sets until reaching the target function The value is optimal; use the obtained three parameters to perform image enhancement on low-contrast area images, De Vries area images and Weber area images; use gradient domain image enhancement algorithm for image processing on the obtained saturated area images; image enhancement after image enhancement The result image is processed by normalizing the gray range as a whole; the obtained results are processed in parallel and then output.

The invention provides a self-adapting image enhancement method, belonging to the digital image processing technology field. The self-adapting image enhancement method comprises the following steps: setting a threshold according to a gray level scope of an image; carrying out automatic color interval enhancement if contrast is smaller than the threshold; carrying out method enhancement based on histogram equalization and anti-equalization transformation if the contrast is larger than the threshold. According to the self-adapting image enhancement method, an image with low contrast can be enhanced, images with part excessive dark and part excessive bright can be enhanced, robustness is strong, and the image after enhancement has a better visual effect.

The teachings as provided herein relate to a watermark embedded in an image that has the property of being relatively indecipherable under normal light by including a distraction pattern, and yet remains decipherable under infrared illumination when viewed by a suitable infrared sensitive instrument. This infrared mark comprises, a substrate reflective to infrared radiation, a foreground colorant mixture printed as an image upon the substrate, a background colorant mixture and a distraction colorant mixture. The foreground colorant mixture layer in connection with the substrate has a property of strongly reflecting infrared illumination, as well as a property of low contrast under normal illumination against the background colorant mixture as printed in close spatial proximity to the foreground colorant mixture pattern. A distraction colorant mixture is selected to have a substantially negligent effect on the infrared response of the foreground and background color mixtures, but as having a substantially noticeable effect of the visual response of the foreground and background color mixtures when provided as a distraction pattern, such that a resultant collocated image rendered substrate suitably exposed to an infrared illumination, will yield a discernable image evident as a infrared mark to a suitable infrared sensitive device, but remain undecipherable under normal ambient light.

A system and method for measuring distances related to a target object depicted in an image and the construction and delivery of supplemental window materials for fenestration. A captured digital image is obtained containing a scene with a target object whose dimension is to be measured. The digital image may contain a target object dimension identified by one or more ancillary objects and a reference object in the same or different planes. Image processing is performed to find the reference object using known fiducial patterns printed on the reference object, metadata supplied by a user and/or by the detection of colored papers in the scene of the captured image. Adhering objects aid in keeping the reference object applied to an item in the scene such as a wall, while contrast objects aid the image processing to locate the reference object in low contrast reference object/background situations. Once located and measured, the reference object is used to calculate a pixel scale factor used to measure the target object dimensions. Target object dimensions are provided to an automated or semi-automated measurement process, design and manufacturing system such that customized parts are provided to end users.

The invention provides a defect detecting method and a defect detecting device. The method comprises the steps of acquiring an image of a region of interest (ROI); setting two threshold values, performing binarization processing on the ROI image according to normal pixel points of the ROI image with the pixel value within the two threshold values and defect pixel points of the ROI image with the pixel value beyond the two threshold values, so as to obtain a binarization image; performing vertical projection and/or horizontal projection on the defect pixel points of the acquired binarization image so as to obtain projection images of the defect points; and detecting the defects from the projection images. The method can realize detection of low contrast defects.

The invention belongs to the technical field of image processing and provides a neural network-based image adaptive enhancement method. The method uses a neural network to establish a model of nonlinear mapping between an average value and a standard deviation of an image and the enhancement factor of an original image and a high-frequency component of the image. The method comprises the following steps for image adaptive enhancement: calculating the average value and the standard deviation of the image and obtaining the enhancement factor by establishing the nonlinear mapping model; filtering the average value of the image and obtaining a low-frequency component of the image; obtaining the high-frequency component of the image through a difference value of the original image and the low-frequency component; and superposing the high-frequency component and the original image which are multiplied with the enhancement factors respectively to realize the adaptive enhancement of the image. The image adaptive enhancement method has the advantages of achieving small calculation amount and strong real-time, automatically acquiring the enhancement factor according to the average value and the standard deviation of the image, realizing the adaptive enhancement of the brightness and the contrast of the image, remarkably improving the visual effect of low-contrast and low-brightness images, and laying a foundation for image identification.