Eastman-Kodak Company

Hsien-Che Lee

Heemin Kwon

Susan S Young

Martin C Kaplan

Allen K Tsaur

Lori L Barski

Robert Allen Senn

Scott J Daly

Robert M Goodwin

Richard Lawrence VanMetter

Jeanine T Liang

Gina N LaRossa

Hui-Jung Lee

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. Finally, the tone scale slope function is integrated to generate the tone scale curve.

Tone scale processing based on image modulation activity

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.

Method for automatically locating an image pattern in digital images using eigenvector analysis

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.

Digital image processing method for edge shaping

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.

Automatic tone adjustment by contrast gain-control on edges

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.

Method for automatically locating image pattern in digital images

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.

Method for estimating and adjusting digital image contrast

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.

Automatic tone scale adjustment using image activity measures

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.

Constructing tone scale curves

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.

Method for displaying a high quality digital color image on a limited

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.

Interactive dynamic range adjustment system for printing digital images

Digital image noise suppression method using SVD block transform

A method of determining the color of light employed to illuminate the scene in a color image from the image itself, relies upon detecting the color of the specularly reflected light in the image. In a preferred mode of practicing the invention, the color of specularly reflected light is detected by detecting a plurality of sets of points having constant hue and varying saturation on a plurality of differently colored surfaces in the scene, and determining the locus of the intersection of straight lines fit through the color values of the sets of points. The locus of the intersection of the straight lines indicates the color of the scene illuminant. In an application of the method in a color printer, the determined illuminant color is employed as the aim point in the photographic printing process.

Method for determining the color of a scene illuminant from a color image

In a method for processing digital color images derived from photographic film, the method employing color reproduction functions generated by normalizing random samples of color values from different colors, oversampling of color values from color saturated textures such as green grass and blue sky noise can distort the randomness of sampling and result in unwanted color shifts in the processed digital images. The problem is solved by constraining the shapes of and relationships between the color reproduction functions in conformance with a priori knowledge of the photographic film, and photographic conditions. According to one aspect of the invention, the shapes of the color reproduction functions are constrained in conformance with the D-Log H curves of a color photographic film. According to another aspect of the invention, the relationships between the color reproduction functions are constrained in conformance with the assumption that the illuminant employed to expose the film was a black body radiator.

Growing community of inventors

Penfield, NY, United States of America

Hsien-Che Lee