Abstract: An automatic white balance (AWB) method is performed on an image to adjust color gains of the image. The image is pre-processed into a set of pre-processed pixels, each of which represented by tricolor values that include a red (R) value, a green (G) value and a blue (B) value. For each candidate illuminant in a set of candidate illuminants, an indicator value that has a diffuse component and a specular component is calculated. One of the candidate illuminants is identified as a resulting illuminant, for which the indicator value is the minimum indicator value among the candidate illuminants. The minimum indicator value corresponds to cancellation of the specular component. The color gains of the image is then adjusted according to color ratios derived from the resulting illuminant.

Abstract: An automatic white balance (AWB) method is performed on an image to adjust color gains of the image. The image is pre-processed into a set of pre-processed pixels, each of which represented by tricolor values that include a red (R) value, a green (G) value and a blue (B) value. For each candidate illuminant in a set of candidate illuminants, an indicator value that has a diffuse component and a specular component is calculated. One of the candidate illuminants is identified as a resulting illuminant, for which the indicator value is the minimum indicator value among the candidate illuminants. The minimum indicator value corresponds to cancellation of the specular component. The color gains of the image is then adjusted according to color ratios derived from the resulting illuminant.

Abstract: An automatic white balance (AWB) method is performed on an image to adjust color gains of the image. The illuminant of the image is determined among a set of candidate illuminants, where each candidate illuminant is described by a corresponding coordinate pair (p, q) in a chromaticity coordinate system. The illuminant of the image can be determined by calculating an indicator value for each candidate illuminant; determining a threshold for indicator values of the candidate illuminants; identifying a subset of the candidate illuminants that have the indicator values not greater than the threshold; and for all candidate illuminants in the subset, calculating a weighted average of corresponding coordinate pairs to obtain an averaged coordinate pair. Chromatic adaptation of the illuminant in the PQ domain can also be performed on the image.

Abstract: A light locus of an imaging system is generated in a chromaticity space of two dimensions. The light locus represents a collection of candidate illuminants. The imaging system captures a gray-card image under each of N light sources to obtain N points in the chromaticity space, wherein N is a positive integer no less than three. Each point in the chromaticity space is described by a coordinate pair calculated from red (R), green (G) and blue (B) tristimulus values of the point. A second order polynomial function is calculated by curve-fitting the N points, and the light locus is generated to represent the second order polynomial in the chromaticity space. One of the candidate illuminants from the light locus is then identified as an illuminant for an image captured by the imaging system. A method for color transformation between two imaging systems in a chromaticity space is also described.

Abstract: A method and an associated device construct a uniform color space from raw RGB data without going through any intermediate color space, such as CIEXYZ. A direction and a scale of each of first, second and third perceptual color axes may be determined based at least in part on the characteristics related to the imaging device, such that a first perceptual color axis correlates with lightness, a second perceptual color axis correlates with yellow-blue color variations, and a third perceptual color axis correlates with red-green color variations. The second perceptual color axis may be substantially aligned with typical daylight variation.

Abstract: A method for correcting color shading artifact of an image and associated apparatus is provided. The method comprises: providing a plurality of bases, calculating a plurality of coefficients respectively associated with the bases, generating a correction map by summing the bases respectively weighted by with the coefficients, and providing a corrected image by correcting the image according to the correction map. Each basis is capable of providing a basic correction value for each pixel of an intermediate image which is associated with the image.

Abstract: A method of constructing uniform color space directly from raw camera RGB and associated device is described. The method constructs a uniform color space from raw camera RGB may determine characteristics related to the imaging device. The method may also determine a direction and a scale of each of first, second and third perceptual color axes based at least in part on the characteristics related to the imaging device, such that a first perceptual color axis correlates with lightness, a second perceptual color axis correlates with yellow-blue color variations, and a third perceptual color axis correlates with red-green color variations. The second perceptual color axis may be substantially aligned with typical daylight variation.

Abstract: A method for correcting color shading artifact of an image and associated apparatus is provided. The method comprises: providing a plurality of bases, calculating a plurality of coefficients respectively associated with the bases, generating a correction map by summing the bases respectively weighted by with the coefficients, and providing a corrected image by correcting the image according to the correction map. Each basis is capable of providing a basic correction value for each pixel of an intermediate image which is associated with the image.

Abstract: A method and an apparatus of processing a plurality of color components of a pixel are provided. The method includes: finding a maximum value of the color components; determining a scaling factor and an adjusting value according to the maximum value; and adjusting each of the color components according to a multiplication utilizing the scaling factor and an addition utilizing the adjusting value. The apparatus includes: a maximum value determining module, for finding a maximum value of the color components; a first adjustment control module, coupled to the maximum value determining module, for determining a scaling factor and an adjusting value according to the maximum value; and an adjusting module, coupled to the first adjustment control module, for adjusting each of the color components according to a multiplication utilizing the scaling factor and an addition utilizing the adjusting value.

Abstract: A method for generating an image-dependent tone scale curve includes (a) computing an image activity histogram for an input image value as a function of the number of neighboring pixels that have a predetermined amount of image modulation exclusive of a predetermined noise modulation; and (b) constructing an image-dependent tone scale curve from the image activity histogram. The predetermined amount of image modulation may comprise any image value within two specified intervals each bounded by a noise threshold and a modulation threshold. In another aspect, a tone scale curve that optimizes the rendering of image activity is generated under one or more constraints. More particularly, one or more slope-limit constraints and/or point constraints are established and then a tone scale function is constructed by applying the slope-limit constraints in a tone scale derivative space. An image activity histogram is computed and applied to the tone scale slopefunction.

Abstract: A method for automatically locating instances of a specified image structure in digital images, comprising the steps of: providing a digital image; detecting simple features associated with the specified image structure in the digital image; for each detected feature, searching, in its spatial neighborhood, for a second or a plural of other features associated with the specified image structure; for each pair of plural of features detected, using an eigenvector classifier to distinguish the wanted features and the unwanted features by matching the eigenvector representation that is constructed from a set of training profiles; and labeling those image regions that are found to be consistent with the specified image structure in the classifying step.

Abstract: A digital image processing method, includes the steps of: transforming the digital image using an edge sensitive wavelet transform to produce a plurality of wavelet coefficients at various resolutions and a residual image; modifying the wavelet coefficients as a function of the image gradient and its rate of change at a resolution corresponding to the respective wavelet coefficient; and inverse transforming the modified wavelet coefficients and the residual image to produce a processed digital image.

Abstract: A method of reducing the dynamic range of an input image for effectively rendering the input image on an output display medium, the method comprises the steps of extracting detail signals and coarse signals from the input image; generating contrast gain-control signals from the input image by detecting coarse scale edges; modifying the detail signals according to the contrast gain signals; and, adding the modified detail signals to the coarse signals to obtain an output image.

Abstract: A method for automatically locating instances of a target pattern in digital images, comprising the steps of: providing a digital image; detecting a first simple feature associated with the target patterns in the digital image; for each detected feature, searching, in its spatial neighborhood, a second or a plural of other features associated with the target patterns; for each pair of plural of features detected, checking the consistency of image intensity profile with the target pattern within the spatial neighborhood as delimited by the feature points detected in the searching and detecting steps; and labeling those image regions that are found to be consistent with the structure of the target pattern in the checking step.

Abstract: A method of estimating the scene contrast from a digital image, the method comprises the steps of: forming a Laplacian histogram distribution; determining, from the Laplacian histogram, first and second thresholds which eliminate substantially uniform areas or a substantially textured portion of the digital image; selecting pixels which are based on the first and second thresholds from the digital image; forming a histogram from the sampled pixels; computing a standard deviation of the sampled histogram; and estimating contrast of the digital image by comparing the computed standard deviation with a predetermined contrast for determining contrast of the input image in relationship with the predetermined contrast.

Abstract: A method for detecting the characteristics of a display, comprises the steps of: creating a model of the display having multiple parameters relating the physical output of the display to an input signal creating the display; employing a model of the human visual system, generating a set of control signals for producing patterns in the display that enable an observer to employ visual selection criteria to identify specific patterns corresponding to specific values of the parameters; applying the set of control signals to the display to produce the patterns; selecting specific patterns from the display; determining the values of the parameters from the patterns selected; and determining the characteristics of the display by applying the values of the parameters to the model.

Abstract: A method of automatically adjusting the tone scale adjustment for digital radiographic images. The method includes the steps of providing an input digital radiographic image; estimating the image noise as a function of code value of the image; determining the range of code values in the input image; determining which way the input image should be processed; determining a bounding box which contains the region of interest of the input image; computing the image activity histogram of the input image; determining four points from the image activity histogram; constructing the tone-scale curve for the input image; and mapping the input image through the tone-scale curve to produce an output image with good tone scale.

Abstract: A method is disclosed for constructing a tone scale curve. In this curve, equal log exposure differences in an x-ray image of an object produce substantially equal brightness differences in a displayed image.

Abstract: Instead of trying to minimize some cost function of color errors, the present invention exploits physical models of scene-image relations. An image is first segmented into regions corresponding to objects in the physical world. Each object is then painted with a few levels of shades of the same chromaticity. This is motivated by the observation that physical objects are the basic units for human visual perception. Chromatic variations within an object surface tend to be grossly discounted by our color vision, and therefore, it is not necessary to render those variations accurately. Furthermore, since the shading across an object surface spans a much smaller dynamic range as compared to the whole scene, the limited number of color shades can be more effectively used within a segmented image region.

Abstract: An interactive dynamic range adjustment method for printing digital images, and an implementation system, are disclosed. The method is based on experimental findings about visual photoreceptor adaption and human visual contrast sensitivity. The system adjusts the contrast of the low-frequency component only of the image, preserving (or if one wishes, enhancing) the high-frequency image component in its contrast. The adjustment is controlled by a mapping curve which the user manipulates interactively. The simulated optical print image and the dynamic range adjusted image are displayed side by side on a monitor screen so that the user can make proper selection of parameters to achieve the desired effect. For many images, the system automatically computes good parameters and no further adjustment is needed.