Eastman-kodak Company

Heemin Kwon

Martin C Kaplan

Jeanine T Liang

Hsien-Che Lee

Kenneth A Carlson

David R Cok

Robert M Goodwin

Patrick A Cosgrove

Anthony Scalise

Patrick Andre Cosgrove

Hui-Jung Lee

Mark S Leader

A method of color balance determination for use by a color copying apparatus utilizing a gray estimate established as a functional relationship among at least three basic color density values measured from regions within multiple image frames of a film order. This functional relationship is preferably a fitted line to a set of measured density values from which density values from regions of high color saturation have been excluded. To discriminate these high color saturation regions, the color saturation is determined relative to a gray point calculated as a weighted average of minimum density and image average density values. Further improvement in the gray estimate is achieved by limiting the set of measured density values to regions of high modulance ('edge effect') within the image frames.

Method for color balance determination

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 photofinishing image processing operator detects failures in the performance of a scene balance mechanism on a digitized image and enables a photofinisher to interactively correct for such failures before the failed image has been processed and an unacceptable output image printed. Whenever a failed image is identified, one or more reasonably low resolution versions of the image as processed by the scene balance mechanism are displayed to the photofinishing operator, together with a request for image adjustment information that is used to modify or correct the color balance of the image. In response to this user input information, the image processor adjusts color balance parameters of the scene balance mechanism, so that the digitized image processed by the modified scene balance mechanism will yield a print of acceptable color balance quality. This interaction of the photofinishing operator with the image processing system may be accomplished by way of several modes, including displaying differently processed versions of the image, or a single version of the image processed by the scene balance mechanism, together with a question regarding the type of image. The failure detection mechanism preferably employs a non-linear discriminator, such as a neural network.

User-interactive reduction of scene balance failures

The method of the present invention reduces noise in digital photographic images by decomposing the digital image into smooth regions and edge regions and by electronically blurring the smooth regions. Smooth regions are areas lacking any sharp detail, such as blue sky. Edge regions are regions containing sharp detail, such as grass. The method operates by choosing a set of neighborhood pixel types from a matrix of pixels representing an image. For each target pixel in the image, all neighborhoods that include the target pixel are considered and a linear least squares regression is computed to fit a plane to the code values of the pixels in each neighborhood, resulting in a goodness of fit and least squares estimate for the code value at the target pixel. Finally, a noise reduced code value for the target pixel is computed as a normalized, weighted sum of the least squares estimates for the target pixel, summed over all the neighborhoods containing the target pixel. The weights are determined as a function of the goodness of fit and the neighborhood type.

Computer based digital image noise reduction method based on

The present invention reduces noise in digital photographic images based on the assumption that images may be decomposed into two types of regions, smooth regions and edge regions. Smooth regions are areas of the image lacking any sharp detail, such as blue sky. Edge regions are regions containing sharp detail, such as edges and textured regions (such as grass). The present method reduces noise in the smooth regions by a mathematical blurring technique based on least squares regression. The blurring does not degrade the sharpness of the image, because there are no sharp details in the smooth regions. Edge regions are left undisturbed to maintain sharpness, but the noise is less noticeable in those regions than in the smooth regions. The method operates upon the luminance and chrominance component of a digital image.

Digital image noise reduction of luminance and chrominance based on

The method of the present invention reduces noise in digital photographic images by decomposing the digital image into smooth regions and edge regions and by electronically blurring the smooth regions. Smooth regions are areas lacking any sharp detail, such as blue sky. Edge regions are regions containing sharp detail, such as grass. The method operates by choosing a set of neighborhood pixel types from a matrix of pixels representing an image. For each target pixel in the image, all neighborhoods that include the target pixel are considered and a linear least squares regression is computed to fit a plane to the code values of the pixels in each neighborhood, resulting in a goodness of fit and a least squares estimate for the code value at the target pixel. Finally, a noise reduced code value for the target pixel is computed as a normalized, weighted sum of the least squares estimates for the target pixel, summed over all the neighborhoods containing the target pixel. The weights are determined as a function of the goodness of fit and the neighborhood type.

Computer based digital image noise reduction method based on overlapping

In a color photographic printer of the type having a film scanner having a 50% green spectral response between 522.+-.5 nm and 570.+-.2.5 nm and peaking at 550 nm, a lowpass cutoff filter for lowering the upper cutoff wavelength of the green channel response is added to the green channel to improve the color correction of the printer for all color films presently in use.

Photographic color printer

A luminance signal is formed from an original color signal and is used to determine a new luminance. The original color signal is then adjusted to have the new luminance in a way that does not change the hue or saturation of the original color. The method is embodied in an analog or a digital circuit and in the alternate may be may be emulated in a general purpose computer. The method permits a change in color signals which affect the overall brightness only in a non-linear way, while adding only a small amount of additional hardware or computation to implement the invention.

Method for adjusting the luminance of a color signal

The present invention efficiently and quickly computes the fit parameters for a noise reduction method wherein a digital image is first decomposed into two types of regions; smooth regions and edge regions. Within the digitized image a plane fit parameter is computed at every point. The computation of the fit parameters incorporates convolutions carried out in an efficient manner by the use of a slide sum technique.

System and method for reducing digital image noise

A pseudo center weighted local variance in the neighborhood of an image pixel determines the amplification factor multiplying the difference between the image pixel and its blurred counterpart before it is combined with the original image. The amplification factor is computed for each pixel in the image, and varies from a minimum value of about -1 when the center weighted variance is at a minimum value to some maximum value when the center weighted variance reaches a maximum value. The pseudo center weighted variance is computed by convolving the local neighborhood surrounding each image pixel with a kernel of center weights having a maximum value at the center pixel. The pseudo center weighted variance is the difference between the center weighted mean of the source of the local neighborhood of image pixels and the square of the center weighted mean. Both image sharpening and noise suppression is achieved in the same image by permitting the amplification factor to vary between a fraction of unity and greater than unity as the center weighted variance varies from a low value (uniform image) to a higher value (textured image).

Unsharp masking using center weighted local variance for image

Digital image noise suppression method using SVD block transform

A block transform image sharpening method employs singular value decomposition to boost texture and edge detail while not boosting shaded (uniform) areas of the image characterized by a lower signal-to-noise ratio while at the same time reducing noise in other areas of the same image not containing much edge detail or texture.

Digital image sharpening method using SVD block transform

A digital color image processing method includes generating color reproduction functions by normalizing samples of color values from the image, and processing the color components of the color digital image by applying the color reproduction functions to the respective color components of the digital image. The samples of color values are selected from the digital image by operating on a luminance component of the digital color image with an image detail detection operator to detect regions of the image representing luminance detail, and operating on the color components of the digital image in the luminance detail regions with a contrast detection filter to detect the contrast of the color components of the image in the luminance detail regions. The color values in the luminance detail regions are separated into contrast intervals and histograms in the contrast intervals are formed. One of the contrast intervals is selected based on the similarity in shape of the color value histograms in the contrast interval. The color reproduction functions are formed by normalizing the histograms in the selected contrast interval.

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Pittsford, NY, United States of America

Heemin Kwon