299 results about "Acutance" patented technology

A method and system for image quality assessment is disclosed. The image quality assessment method is a no-reference method for objectively assessing the quality of medical images. This method is guided by the human vision model in order to accurately reflect human perception. A region of interest (ROI) of medical image is divided into non-overlapping blocks of equal size. Each of the blocks is categorized as a smooth block, a texture block, or an edge block. A perceptual sharpness measure, which is weighted by local contrast, is calculated for each of the edge blocks. A perceptual noise level measure, which is weighted by background luminance, is calculated for each of the smooth blocks. A sharpness quality index is determined based on the perceptual sharpness measures of all of the edge blocks, and a noise level quality index is determined based on the perceptual noise level measures of all of the smooth blocks. An overall image quality index can be determined by using task specific machine learning of samples of annotated images. The image quality assessment method can be used in applications, such as video/image compression and storage in healthcare and homeland security, and band-width limited wireless communication.

A method is provided for computing a composite image representing a focused image of an object in an application of machine vision in an optical inspection system. An image tessellated into focus regions is evaluated by region for fine feature sharpness. A sharpness image is computed for each focused region using a fine feature sharpness measurement. A focused composite image is computed by combining as a weighted average, the images at several focus settings, using the sharpness image at each focus setting as the weight. The focused composite image can be further analyzed, inspected, or otherwise processed.

A video signal processor includes an edge enhancement determining circuit that senses the amount of some characteristic of that signal which provides a measure of noise inherent in the signal, precisely determines a preferred amount of edge enhancement to be applied to a video signal as a function of that characteristic, and applies that amount of edge enhancement to the video signal. The video signal characteristic, used to determine the noise content of the video signal includes the amount of automatic gain control amplification and the brightness of the viewed scene. Thus, the amount of edge enhancement added to the video signal is adaptively controlled according to the brightness level of the viewed scene and the amplification level of the video signal by the automatic gain control circuit, and provides the proper amount of edge sharpness to the viewed video scene as a function of its noise content. This provides an increase in perceived sharpness of the viewed image, without an increase in background noise that occurs in conventional non-adaptive systems.

A method of measuring eye refraction to achieve desired quality according to a selected vision characteristics comprising the steps of selecting a characteristic of vision to correlate to the desired quality of vision from a group of vision characteristics comprising acuity, Strehl ratio, contrast sensitivity, night vision, day vision, and depth of focus, dynamic refraction over a period of time during focus accommodation, and dynamic refraction over a period of time during pupil constriction and dilation; using wavefront aberration measurements to objectively measure the state of the eye refraction that defines the desired vision characteristic; and expressing the measured state of refraction with a mathematical function to enable correction of the pre-selected vision characteristic to achieve the desired quality of vision. The mathematical function of expression may be a Zernike polynomial having both second order and higher order terms or a function determined by spline mathematical calculations. The pre-selected desired vision characteristics may be determined using ray tracing technology.

A vision metric, called the sharpness metric, indicates the subjective sharpness of a patient's vision by taking into account both the wavefront aberration and the retinal response to the image. A retinal image quality function such as the point spread function is convolved by a neural quality function, and the maximum of the convolution over the retinal plane provides the sharpness metric. The sharpness metric can be used to control eye surgery or the fabrication of a lens.

An image scaling system includes a single set of line buffers that receive and store input image pixel data in an input video frame. The scaling system also includes a linear two-dimensional sharpness enhancement unit configured to receive input pixel data from the line buffers and to generate sharpened pixel data by enhancing high frequency components of the input pixel data at an input image resolution, a linear two-dimensional image scaling unit configured to receive the sharpened pixel data and to convert the sharpened pixel data into scaled sharpened pixel data at an output image resolution, and a transient improvement unit configured to receive the input pixel data from the line buffers, sharpened pixel data and scaled sharpened pixel data to improve transient responses at edges in an output image, and to generate output image pixel data at the output image resolution.

A single-ended blur detection probe and method with a local sharpness map for analyzing a video image sequence uses two sets of edge filters, one for “fast edges” and the other for “slow edges.” Each set of edge filters includes a horizontal bandpass filter, a vertical bandpass filter and a pair of orthogonal diagonal filters where the frequency response of the fast edge filters overlap the frequency response of the slow edge filters. The video image sequence is input to each filter of each set, and the output absolute values are combined with weighting factors to produce a slow edge weighted sum array and a fast edge weighted sum arra. The respective weighted sum arrays are then decimated to produce a slow edge decimated array and a fast edge decimated array. The ratio of the maximum difference value between the decimated arrays and the maximum value from the fast edge decimated array, weighted by an appropriate factor, produces a localized maximum sharpness value, the log of which produces a dimensionless blur value.

A method for selecting a digital image having controlled sharpness characteristics from a set of candidate digital images of a common scene, each digital image having different sharpness characteristics. An image segmentation process is used to segment each of the candidate digital images into a subject region and a background region. For each candidate digital image the subject and background regions are analyzed to determine an associated subject and background sharpness levels. An output digital image is selected by comparing the determined subject and background sharpness levels to respective aim subject and background sharpness levels. In some embodiments, the aim subject and background sharpness levels are defined in accordance with a scene type classification.

In a method of removing a noise signal from a video signal and enhancing edge sharpness to improve the video signal definition, a different weight is assigned depending on a characteristic of input pixels. To this end, at least two pixel blocks including the input and at least two adjacent pixels. A pixel difference between the pixels in the each of the pixel blocks is calculated and one of the calculated pixel differences is selected. The input pixel is assigned with a weight corresponding to the selected pixel difference. Accordingly, the definition of the video signal is improved.

The invention discloses an infrared and visible light image signal processing method and an implementation device thereof. The method comprises the following steps that an infrared image and a visible light image corrected in an infrared mode are provided; a color feature value corresponding to each pixel in the visible light image is calculated; acutance information corresponding to each pixel in the visible light image is calculated by means of the corresponding color feature value; acutance information corresponding to each pixel in the infrared image is calculated; acutance correction parameters and brightness correction parameters of the visible light image and the infrared image are calculated; according to the acutance correction parameters and the brightness correction parameters which are obtained through calculation, one-by-one pixel weighted combination is conducted on a certain color space. Due to the fact that acutance analysis, color analysis and brightness analysis are conducted on the infrared image and the visible light image corrected in the infrared mode, the infrared image and the visible light image are dynamically fused into one image, and the brightness, colors and acutance of the output image have no loss.

The sharpness of a digital image is adjusted according to defined aim subject and background sharpness levels. An image segmentation process is used to segment an input digital image into a subject region and a background region. The subject and background regions are analyzed to determine corresponding subject and background sharpness levels. An enhanced digital image is formed wherein the sharpness of the subject region is adjusted responsive to the subject sharpness level and the aim subject sharpness level, and the sharpness of the background region is adjusted responsive to the background sharpness level and the aim background sharpness level. In some embodiments, the input digital image is analyzed to determined a scene type classification and the aim subject and background sharpness levels are defined in accordance with the determined scene type classification.

A system and process for improving image quality is described. The process uses an edge map to smooth colors based on at least one of how close a pixel is to an edge and the strength of the edge.

The invention provides a three-dimensional photographic device. The three-dimensional photographic device comprises two camera modules and a processor, wherein each camera module comprises a camera lens unit and an image sensor, the camera lens unit utilizes a shape memory alloy element as a variable focal length actuator, and the image sensor is used for forming images with at least two colors. The processor comprises a full width focusing module, a focusing driving module and an image synthesis module, the full width focusing module is used for judging an object distance according to acutance of colors, judging whether the object distance is larger than a certain preset distance or not, when the object distance is larger than the preset distance, the images are conducted sharpening processing and the images become distinct, otherwise a control signal is sent to the focusing driving module, the camera modules are driven to change a focal distance to directly acquire distinct images after the focusing driving module receives the control signal, and the image synthesis module is used for utilizing the distinct images to synthesize three-dimension images. The three-dimensional photographic device utilizes a full width focusing technology, and therefore the distinct images can be further acquired in a close distance.

An image is divided into areas in accordance with degrees of focusing. As the divided areas, there are a focus area and a non-focus area. With respect to the non-focus area, the image thereof is processed so as to emphasize its blur. In contrast, with respect to the focus area, the image thereof is processed so as to emphasize its sharpness. The processed image is displayed on a monitor so that perspective is expressed like a single-lens reflex.

An image/video may be analyzed to determine quality of its attributes, including local, global and pixel colorfulness, sharpness, and contrast to obtain an image quality measure. Invented quality may be obtained for captured images or videos and compared to a database or reference value of quality measures to identify quality of products, component anomalies, and product matches.

The invention discloses a circumference SAR back projection self-focusing method based on subaperture synthesis. The method includes the steps of dividing circumference SAR full-aperture echo data into subaperture echo data, selecting a strong scattering unit in an observation scene to conduct back projection self-focusing processing based on image acutance maximization so that the phase error of each subaperture echo datum can be estimated, synthesizing the phase errors of all the subaperature echo data into the circumference SAR full-aperture echo data phase error, and finally obtaining the circumference SAR full-aperture echo data phase error so that the accurate circumference SAR back projection imaging can be conducted. Compared with an existing circumference SAR back projection self-focusing algorithm, the method has the advantages of being high in imaging accuracy and speed and is suitable for processing circumference SAR large-scene measured data.

There is provided an image processing device that acquires restored image data by performing restoration processing using a restoration filter based on the PSF of an optical system for original image data acquired by capturing a subject image using the optical system. This device includes a restoration processing unit 38 that performs restoration processing by applying the restoration filter to the original image data, a quasi-focus region detection unit 50 that detects a quasi-focus region in an original image corresponding to the original image data, and a sharpness restoration control unit 37. The sharpness restoration control unit adjusts the restoration strength magnification U for original image data of the detected quasi-focus region so as to be smaller than the restoration strength magnification U for original image data of at least a focus region.

A halftoning method to improve the sharpness of a boundary region, including detecting a direction of the boundary region, selecting a halftone table indicating a direction that is closest to a direction perpendicular to the detected direction from among predetermined halftone tables, and halftoning the pixel data using the selected halftone table, and an apparatus to perform the method.

Electronic images that are degraded by noise and data reduction, such as MPEG encoding, display artifacts in the reproduced image, such as ringing (“ripples”) and blocks (“huge pixels”), and noise in the image may be apparent as graininess. By performing image analysis, both on a frame-by-frame and pixel-by-pixel basis it is possible to identify and separate edges in the image, ringing artifacts and the boundaries between block transitions. By applying noise reduction according to the analysis, followed by sharpness enhancement, it is possible to clean up the image for further utilization.