Abstract: A lens assembly includes a plurality of component lens elements, and a fiber optic face plate having a back surface and a non-planar front surface. The plurality of component lens elements are configured to direct a focused image onto the non-planar front surface of the fiber optic face plate, and the fiber optic face plate is configured to transmit the focused image through the back surface.

Abstract: A lens assembly includes a plurality of component lens elements, and a fiber optic face plate having a back surface and a non-planar front surface. The plurality of component lens elements are configured to direct a focused image onto the non-planar front surface of the fiber optic face plate, and the fiber optic face plate is configured to transmit the focused image through the back surface.

Abstract: The symbolic differentiation technique described herein uses operator overloading and two simple recursive procedures, both the forward and reverse forms of differentiation, to create purely symbolic derivatives. The symbolic derivative expressions can be translated into a program in an arbitrary source language, such as C# or C++, and this program can then be compiled to generate an efficient executable which eliminates much of the interpretive overhead normally encountered in automatic differentiation.

Abstract: The symbolic differentiation technique described herein uses operator overloading and two simple recursive procedures, both the forward and reverse forms of differentiation, to create purely symbolic derivatives. The symbolic derivative expressions can be translated into a program in an arbitrary source language, such as C# or C++, and this program can then be compiled to generate an efficient executable which eliminates much of the interpretive overhead normally encountered in automatic differentiation.

Abstract: Demosaicing of graphical content is provided. In an illustrative implementation a demosaicing engine executing one or more demosaicing algorithms is employed to operate on graphical content to provide better quality and higher resolution images. In operation, the demosaicing engine operates in two modes, a training/learning mode, and a run time mode. During training, training-images are analyzed to generate a codebook of mosaic filter table entries, such that each table entry has an associated list of similar training pixel blocks and their associated filters. During run time, a run-time image is broken into pixel blocks. Each pixel block is then compared with the entries of the codebook to find the closest match filter. The list associated with the entry is then processed using a least-squares algorithm to locate the optimal mosaic filter. As a result, higher resolution is achieved without requiring more pixels.

Abstract: Demosaicing of graphical content is provided. In an illustrative implementation a demosaicing engine executing one or more demosaicing algorithms is employed to operate on graphical content to provide better quality and higher resolution images. In operation, the demosaicing engine operates in two modes, a training/learning mode, and a run time mode. During training, training-images are analyzed to generate a codebook of mosaic filter table entries, such that each table entry has an associated list of similar training pixel blocks and their associated filters. During run time, a run-time image is broken into pixel blocks. Each pixel block is then compared with the entries of the codebook to find the closest match filter. The list associated with the entry is then processed using a least-squares algorithm to locate the optimal mosaic filter. As a result, higher resolution is achieved without requiring more pixels.

Abstract: Demosaicing of graphical content is provided. In an illustrative implementation a demosaicing engine executing one or more demosaicing algorithms is employed to operate on graphical content to provide better quality and higher resolution images. In operation, the demosaicing engine operates in two modes, a training/learning mode, and a run time mode. During training, training-images are analyzed to generate a codebook of mosaic filter table entries, such that each table entry has an associated list of similar training pixel blocks and their associated filters. During run time, a run-time image is broken into pixel blocks. Each pixel block is then compared with the entries of the codebook to find the closest match filter. The list associated with the entry is then processed using a least-squares algorithm to locate the optimal mosaic filter. As a result, higher resolution is achieved without requiring more pixels.

Abstract: A method and apparatus for compressing and decompressing texture patterns using a predefined two or more stage codebook with stored texture block vectors. The method of compressing includes selecting a texture block in the uncompressed texture pattern and determining a distortion value of the selected texture block as compared to a texture block vector stored in the two or more stage codebook. The distortion determination is repeated for a different texture block stored in the two or more stage codebook, until the determined distortion value is the smallest for all texture block vectors stored in the two or more stage codebook. The indices of the stored texture block in the two or more stage codebook are retrieved and stored. The retrieved indices are stored in an index block within an index map, wherein the index blocks correspond to texture blocks in the texture pattern. The two or more stage codebook is generated from a representative texture pattern.