Eastman-kodak Company

Carestream Health, Inc.

Wei Hao

Jiebo Luo

Alexander C Loui

Bruce Harold Pillman

Cathleen D Cerosaletti

Dale Frederick McIntyre

Andrew Charles Gallagher

Zhimin Huo

Dhiraj Joshi

Jie Yu

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.

Controlling the sharpness of a digital image

A method for determining an estimated clutter level of an input digital image based on an inequality index. The inequality index is determined by analyzing the input digital image to determine a set of image features. The image features are associated with a set of designated reference features, and the inequality index is determined based on the statistical variation of the reference features. A set of scene content features relating to spatial structures or semantic content of the input digital image is determined by analyzing the input digital image. The estimated clutter is determined responsive to the inequality index and the scene content features.

Determining the estimated clutter of digital images

A method for determining an estimated clutter level of an input digital image based on an inequality index. The inequality index is determined by partitioning the input digital image into small sub-images and analyzing the sub-images to determine a set of image features. The image features are associated with a set of designated reference features, and the inequality index is determined based on the statistical variation of the reference features. The inequality index is compared to a predefined threshold to classify the input digital image as a rich-content image or a low-content image. For rich-content images, the estimated clutter level is determined responsive to a set of scene content features relating to spatial structures or semantic content of the input digital image is determined by analyzing the input digital image. For low-content images, the estimated clutter level is determined responsive to an overall luminance level.

Estimating the clutter of digital images

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.

Adjusting the sharpness of a digital image

A method of recognizing an event depicted in an image from the image and a location information associated with the image is disclosed. The method includes acquiring the image and its associated location information; using the location information to acquire an aerial image(s) correlated to the location information; identifying the event using both the image and the acquired aerial image(s); and storing the event in association with the image for subsequent use.

Event recognition using image and location information

A method for producing an improved high resolution image is disclosed including capturing low resolution images and a high resolution image; combining the low resolution images to provide an aggregate low resolution image; reducing the resolution of the high resolution image and then interpolated to produce a blurred high resolution image; calculating an image difference map using the aggregate high resolution image and blurred high resolution image; and using the image difference map along with the aggregate high resolution image and the high resolution image to produce an improved high resolution image.

Providing improved high resolution image

A method of recognizing the environment of an image from an image and position information associated with the image includes acquiring the image and its associated position information; using the position information to acquire an aerial image correlated to the position information; identifying the environment of the image from the acquired aerial image; and storing the environment of the image in association with the image for subsequent use.

Recognizing image environment from image and position

A method for determining a candidate lesion region within an ultrasound image. The method includes the steps of: accessing a digital ultrasound image of anatomical tissue; segmenting spatially contiguous pixels in the ultrasound image into a plurality of regions in accordance with substantially similar intensity values and spatial smoothness constraints; and selecting, from the plurality of regions, one or more candidate lesion regions having an intensity value lower than a pre-determined intensity value. In one arrangement, the one or more candidate lesion region is classified into at least one of the following classes: benign, malignant, or unknown.

Detecting and classifying lesions in ultrasound images

A method of detecting sky in a digital color image having pixels is disclosed. The method includes identifying pixels from the digital color image representing an initial sky region; developing a model based on the identified sky pixels, wherein such model is a mathematical function that has inputs of pixel position and outputs of color; and using the model to operate on the digital color image to classify additional pixels not included in the initial sky region as sky.

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Webster, NY, United States of America

Wei Hao