Abstract: A system for capturing color images comprising an image sensor with an array of light sensitive photosites of a plurality of colors. Each color has its own spectral sensitivity. Colors with a substantially low color separation are assigned a substantially high density of pixels in the photosite array and colors with a substantially high color separation are assigned a substantially low density of pixels in the photosite array. A digital image signal processor is adapted to receive a raw mosaicked image from said image sensor when the image sensor is impacted with light, and to reconstruct a full color image from the raw image data. Optionally, the raw image data is demosaicked, a chroma denoiser is applied to the image data and the image data is converted to a specified color space, wherein application of the chroma denoiser and conversion to a specified color space are performed in any order.

Abstract: A high sensitivity image sensor with panchromatic and color pixels arranged to enable high sensitivity, efficient binning and minimize aliasing is disclosed. Efficient binning is enabled by composing the color filter array of tiles containing pixels of at most two types, one panchromatic and the other colored, so that like color pixels to be binned are adjacent to each other. Aliasing is minimized by the twin methods of evenly spacing out tiles containing pixels of like color over the image sensor, and optimizing the arrangement of panchromatic and color pixels within each tile if the tile contains both panchromatic and colored pixels. An image sensor with these color filter arrays can be used to realize cameras with high sensitivity and a plurality of resolutions, with the lower resolutions consuming less power, while minimizing alias artifacts in each supported resolution. A variant of this image sensor with both infrared and color pixels is also disclosed.

Abstract: A high sensitivity image sensor with panchromatic and color pixels arranged to enable high sensitivity, efficient binning and minimize aliasing is disclosed. Efficient binning is enabled by composing the color filter array of tiles containing pixels of at most two types, one panchromatic and the other colored, so that like color pixels to be binned are adjacent to each other. Aliasing is minimized by the twin methods of evenly spacing out tiles containing pixels of like color over the image sensor, and optimizing the arrangement of panchromatic and color pixels within each tile if the tile contains both panchromatic and colored pixels. An image sensor with these color filter arrays can be used to realize cameras with high sensitivity and a plurality of resolutions, with the lower resolutions consuming less power, while minimizing alias artifacts in each supported resolution. A variant of this image sensor with both infrared and color pixels is also disclosed.

Abstract: Methods and systems for robust and flexible extraction of image information using color filter arrays. Methods are provided comprising receiving a sample set of data generated by transforming and sampling an optical property of an original color image in a spatial basis, wherein the transformation effected is substantially local in the spatial basis and has partially overlapping spectra. A generalized inverse of the transform augmented with regularization constraints is applied to the sample set of data to infer original image data. According to one approach the generalized inverse does not use variational minimization or assume constant color ratios. According to an alternative approach regularization can take the form of predetermined spectral constraints applied to the sample set of data to infer the original image data. According to another alternative approach regularization can promote spectral sparsity.

Abstract: A method for coding color images with fewer blue samples than samples of other colors. This provides a psycho visually high image quality since the human retina itself has fewer S cones than L, M cones. Applications include image and video coding with lower density of blue samples than other colors such as red and green. Another application is in single sensor multi-spectral and color cameras that use Color Filter Arrays. Sampling density is limited in Color Filter Arrays so that a lower blue sample density enables higher sample densities of other colors.

Abstract: A method for coding color images with fewer blue samples than samples of other colors. This provides a psycho visually high image quality since the human retina itself has fewer S cones than L, M cones. Applications include image and video coding with lower density of blue samples than other colors such as red and green. Another application is in single sensor multi-spectral and color cameras that use Color Filter Arrays. Sampling density is limited in Color Filter Arrays so that a lower blue sample density enables higher sample densities of other colors.

Abstract: Methods and systems for robust and flexible extraction of image information using color filter arrays. Methods are provided comprising receiving a sample set of data generated by transforming and sampling an optical property of an original color image in a spatial basis, wherein the transformation effected is substantially local in the spatial basis and has partially overlapping spectra. A generalized inverse of the transform augmented with regularization constraints is applied to the sample set of data to infer original image data. According to one approach the generalized inverse does not use variational minimization or assume constant color ratios. According to an alternative approach regularization can take the form of predetermined spectral constraints applied to the sample set of data to infer the original image data. According to another alternative approach regularization can promote spectral sparsity.

Abstract: A method and system for compressive color image sampling and reconstruction. A sample set of data generated by transforming and sampling an optical property of an original image in a spatial basis, wherein the transformation effected is substantially diagonal in the spatial basis is received. A compressive sensing reconstruction technique is applied to the sample data to produce a set of inferred original image data. A data processing apparatus is adapted to receive such a sample set of data and apply a compressive sensing reconstruction technique to the sample data to produce a set of inferred original image data. In a preferred embodiment a imaging system uses a color filter array (CFA) wherein colors are randomly or pseudo-randomly arranged over an image sensor. The image is inferred from the sensor data and the CFA pattern using compressive sensing sparse solution search techniques.

Abstract: The present invention relates to spectral image sampling. An image is projected onto an array of optical transformation devices. Image intensity values transmitted by said array of optical transformation devices are detected at each sensor element of an image sensor. The image sensor comprises a plurality of photosensitive sensor elements configured to detect a magnitude of incident light, wherein said sensor elements are shaped as regular polygons whose aspect ratio is controlled to minimize sensor element count while obtaining a desired image quality. The detected image intensity values are processed to infer the image.

Abstract: Methods and systems for robust and flexible extraction of image information using color filter arrays. Methods are provided comprising receiving a sample set of data generated by transforming and sampling an optical property of an original color image in a spatial basis, wherein the transformation effected is substantially local in the spatial basis and has partially overlapping spectra. A generalized inverse of the transform augmented with regularization constraints is applied to the sample set of data to infer original image data. According to one approach the generalized inverse does not use variational minimization or assume constant color ratios. According to an alternative approach regularization can take the form of predetermined spectral constraints applied to the sample set of data to infer the original image data. According to another alternative approach regularization can promote spectral sparsity.

Abstract: The present invention relates to spectral image sampling. An image is projected onto an array of optical transformation devices. Image intensity values transmitted by said array of optical transformation devices are detected at each sensor element of an image sensor. The image sensor comprises a plurality of photosensitive sensor elements configured to detect a magnitude of incident light, wherein said sensor elements are shaped as regular polygons whose aspect ratio is controlled to minimize sensor element count while obtaining a desired image quality. The detected image intensity values are processed to infer the image.

Abstract: A method and system for compressive color image sampling and reconstruction. A sample set of data generated by transforming and sampling an optical property of an original image in a spatial basis, wherein the transformation effected is substantially diagonal in the spatial basis is received. A compressive sensing reconstruction technique is applied to the sample data to produce a set of inferred original image data. A data processing apparatus is adapted to receive such a sample set of data and apply a compressive sensing reconstruction technique to the sample data to produce a set of inferred original image data. In a preferred embodiment a imaging system uses a color filter array (CFA) wherein colors are randomly or pseudo-randomly arranged over an image sensor. The image is inferred from the sensor data and the CFA pattern using compressive sensing sparse solution search techniques.