Descartes Labs, Inc.

Los Alamos National Security, LLC

Rick S Chartrand

Ryan S Keisler

Xander H Rudelis

Matthew T Calef

Brendt Egon Wohlberg

Garrett T Kenyon

Steven P Brumby

Luis Bettencourt

Sparse phase unwrapping is disclosed. A first image and a second image are received. The first image and the second image are coregistered. The first image and the second image comprise respective phase data. An unwrapped interferogram is generated, including by solving an optimization problem using a nonconvex penalty function, where minimizing the penalty function produces sparse minimizers.

Sparse phase unwrapping

A boundary map and a first image are received. The boundary map is used to determine that brightness values for a set of pixels included in the first image should be regularized. The first set of pixels include, at a first pixel position, a first pixel having a first set of brightness values. The first set of pixels further includes, at a second pixel position, a second set of brightness values. An output image is generated by storing, at both a first and second pixel position in the output image, a set of regularized values.

Using boundary maps to refine imagery

A first image is received. An initial label is assigned to at least some pixels in the first image, including by assigning a first label to a first pixel. A determination is made that the first pixel's label should be replaced with a different label. The first pixel's label is updated with the different label.

Iterative relabeling using spectral neighborhoods

A boundary map and a first image are received as input. The boundary map indicates a plurality of fields. The boundary map is used to regularize, across a set comprising a plurality of pixels bounded by a first field in the plurality of fields, a set of one or more pixel brightness values. A regularized image is generated as output, where for each pixel in the first image bounded by the first field, a set of values in the corresponding pixel in the regularized image is assigned the set of regularized pixel brightness values.

Creating a boundary map

A first image is received. An initial label is assigned to at least some pixels in the first image, including by assigning a first label to a first pixel. A determination is made, based at least in part on labels of spectral neighbors of the first pixel, that the first pixel's label should be replaced with a different label. The first pixel's label is updated with the different label. The first pixel's label is iteratively refined until convergence.

Various approaches to image enhancement are disclosed. In one approach, a boundary map and an image are received. The boundary map is used to determine that brightness values for a set of pixels included in the image should be regularized. An output image is generated by storing, at both a first and second pixel position in the output image, a set of regularized values. In another approach, an image to be enhanced and an edge map are received. Edge-masked derivative matrices are constructed and used to iteratively solve a series of linear equations, wherein solutions to the linear equations minimize an increasingly accurate quadratic approximation of a penalty function that measures a total amount of variation in a function, and a derivation of the function from the image. A vector result of the iterative solution is transformed into a raster image.

A first image is received. An initial label is assigned to at least some pixels in the first image, including by assigning a first label to a first pixel. A determination is made, based at least in part on labels of spectral neighbors of the first pixel, that the first pixel's label should be replaced with a different label. The first pixel's label is updated with the different label.

A set of data comprising at least a first tile and a second tile is received. A determination of a measure is made, for a first pixel location included in both the first tile and the second tile, of whether the first pixel location corresponds to a boundary. A boundary map is generated as output. The boundary map can be used, in conjunction with an image, to regularize pixel brightness values of the image.

Various approaches to image enhancement are disclosed. In one approach, an image to be enhanced and a boundary map are received. A field value array is made using the boundary map and the received image. For each field represented in the field value array, pixel values obtained from the received image are aggregated. An enhanced image is generated as output by performing a rereasterization using the aggregated values. In another approach, an image to be enhanced and an edge map are received. Edge-masked derivative matrices are constructed. The edge-masked derivative matrices are used to iteratively solve a series of linear equations, wherein solutions to the linear equations minimize an increasingly accurate quadratic approximation of a penalty function that measures a total amount of variation in a function, and a derivation of the function from the image. A vector result of the iterative solution is transformed into a raster image.

Approaches for deciding what individuals in a population of visual system 'neurons' are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

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Los Alamos, NM, United States of America

Rick S Chartrand