Abstract: A composition includes a plurality of gold nanoparticles each having at least one surface. The gold nanoparticles have an average length of at least about 90 nm and an average width of at least about 25 nm.

Abstract: An apparatus includes a light splitter to receive a light beam and direct a first portion of the light beam to a reference arm and a second portion of the light beam to a sample arm. The sample arm includes a phase scrambler, in a path of the second portion of the light beam, to cause local-random-time varying phase modulation to the second portion of the light beam. The sample arm also includes a controller to change the local phase of the second portion of the light. The apparatus further includes a detector, in optical communication with the reference arm and the sample arm, to detect an interference pattern produced by the first portion of the light beam propagated through the reference arm and the second portion of the light beam scattered from the sample via the sample arm.

Abstract: A composition includes a plurality of gold nanoparticles each having at least one surface. The gold nanoparticles have an average length of at least about 90 nm and an average width of at least about 25 nm.

Abstract: In accordance with an embodiment of the invention, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated.

Abstract: In an embodiment, a device comprises a plurality of elements configured to apply a filter to multiple groups of pixels in a neighborhood of pixels surrounding a particular pixel to generate a matrix of filtered values; compute, from the matrix of filtered values, a first set of gradients along a first direction and a second set of gradients along a second and different direction; determine how many directional changes are experienced by the gradients in the first set of gradients and the gradients in the second set of gradients; compute a first weighted value for a first direction and a second weighted value for a second direction; and based, at least in part, upon the first and second weighted values, compute an overall texture characterization value for the particular pixel, wherein the overall texture characterization value indicates a type of image environment in which the particular pixel is located.

Abstract: In an embodiment, a device comprises a plurality of elements, including logical elements, wherein the elements are configured to perform the operations of: in a neighborhood of pixels surrounding and including a particular pixel, applying a filter to multiple groups of pixels in the neighborhood to generate a set of filtered values; generating, based at least in part upon the set of filtered values, one or more sets of gradient values; based at least in part upon the one or more sets of gradient values, computing a first metric for an image environment in which the particular pixel is situated; determining a second metric for the image environment in which the particular pixel is situated, wherein the second metric distinguishes between a detail environment; and based at least in part upon the first metric and the second metric, computing a gradient improvement (GI) metric for the particular pixel.

Abstract: A chromatic noise reduction method is provided for removing chromatic noise from the pixels of a mosaic image. In one implementation, an actual chroma value and a de-noised chroma value are derived for the central pixel of a matrix of pixels. Based at least in part upon these chroma values, a final chroma value is derived for the central pixel. The final chroma value is then used, along with the actual luminance of the central pixel, to derive a final de-noised pixel value for the central pixel. By de-noising the central pixel based on its chroma (which takes into account more than one color) rather than on just the color channel of the central pixel, this method allows the central pixel to be de-noised in a more color-coordinated fashion. As a result, improved chromatic noise reduction is achieved.

Abstract: In accordance with an embodiment of the invention, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated.

Abstract: A chromatic noise reduction method is provided for removing chromatic noise from the pixels of a mosaic image. In one implementation, an actual chroma value and a de-noised chroma value are derived for the central pixel of a matrix of pixels. Based at least in part upon these chroma values, a final chroma value is derived for the central pixel. The final chroma value is then used, along with the actual luminance of the central pixel, to derive a final de-noised pixel value for the central pixel. By de-noising the central pixel based on its chroma (which takes into account more than one color) rather than on just the color channel of the central pixel, this method allows the central pixel to be de-noised in a more color-coordinated fashion. As a result, improved chromatic noise reduction is achieved.

Abstract: In an embodiment, a device comprises a plurality of elements, including logical elements, wherein the elements are configured to perform the operations of: in a neighborhood of pixels surrounding and including a particular pixel, applying a filter to multiple groups of pixels in the neighborhood to generate a set of filtered values; generating, based at least in part upon the set of filtered values, one or more sets of gradient values; based at least in part upon the one or more sets of gradient values, computing a first metric for an image environment in which the particular pixel is situated; determining a second metric for the image environment in which the particular pixel is situated, wherein the second metric distinguishes between a detail environment; and based at least in part upon the first metric and the second metric, computing a gradient improvement (GI) metric for the particular pixel.

Abstract: In an embodiment, a device comprises a plurality of elements configured to apply a filter to multiple groups of pixels in a neighborhood of pixels surrounding a particular pixel to generate a matrix of filtered values; compute, from the matrix of filtered values, a first set of gradients along a first direction and a second set of gradients along a second and different direction; determine how many directional changes are experienced by the gradients in the first set of gradients and the gradients in the second set of gradients; compute a first weighted value for a first direction and a second weighted value for a second direction; and based, at least in part, upon the first and second weighted values, compute an overall texture characterization value for the particular pixel, wherein the overall texture characterization value indicates a type of image environment in which the particular pixel is located.