Microsoft Technology Licensing, LLC

Charles L Zitnick

Richard S Szeliski

Sing Bing Kang

Matthew Uyttendaele

Ce Liu

Eric Paul Bennett

Simon A J Winder

Jie Sun

A Bayesian two-color image demosaicer and method for processing a digital color image to demosaic the image in such a way as to reduce image artifacts. The method and system are an improvement on and an enhancement to previous demosaicing techniques. A preliminary demosaicing pass is performed on the image to assign each pixel a fully specified RGB triple color value. The final color value of pixel in the processed image is restricted to be a linear combination of two colors. Fully-specified RGB triple color values for each pixel in an image used to find two clusters represented favored two colors. The amount of contribution from these favored two colors on the final color value then is determined. The method and system also can process multiple images to improve the demosaicing results. When using multiple images, sampling can be performed at a finer resolution, known as super resolution.

Bayesian demosaicing using a two-color image

An 'Image Denoiser' provides a probabilistic process for denoising color images by segmenting an input image into regions, estimating statistics within each region, and then estimating a clean (or denoised) image using a probabilistic model of image formation. In one embodiment, estimated blur between each region is used to reduce artificial sharpening of region boundaries resulting from denoising the input image. In further embodiments, the estimated blur is used for additional purposes, including sharpening edges between one or more regions, and selectively blurring or sharpening one or more specific regions of the image (i.e., 'selective focus') while maintaining the original blurring between the various regions.

Region-based image denoising

A feature symbol triplets object instance recognizer and method for recognizing specific objects in a query image. Generally, the recognizer and method find repeatable features in the image, and match the repeatable features between a query image and a set of training images. More specifically, the recognizer and method finds features in the query image and then groups all possible combinations of three features in to feature triplets. Small regions or 'patches' in the query image, and an affine transformation is applied to the patches to identify any similarity between patches in a query image and training images. The affine transformation is computed using position of neighboring features in each feature triplet. Next, all similar patches are found, and then pairs of images are aligned to determine if the patches agree in the position of the object. If they do, then it is said that object is found and identified.

Object instance recognition using feature symbol triplets

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Seattle, WA, United States of America

Charles L Zitnick