282 results about "Demosaicing" patented technology

A method is provided of solving the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. The invention includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed invention is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the invention algorithms, and their strength.

A method for generating an interpolated output image, including generating a first interpolation array including RGB values, at least one of which is interpolated, based on a first interpolation region, and generating a first homogeneity array based on the first interpolation array including homogeneity values calculated based on a first homogeneity neighborhood having the same pixel locations as the first interpolation region. The method includes generating a second interpolation array based on a second interpolation region, and generating a second homogeneity array based on the second interpolation array and a second homogeneity neighborhood, where the second homogeneity neighborhood includes the same pixel locations as the second interpolation region. The method includes determining, for a first pixel location, that the homogeneity value in the first homogeneity array is greater than the homogeneity value in the second homogeneity array, and outputting the RGB color values in the first interpolation array.

A system and method is provided for processing a demosaiced image using a color aliasing artifact reduction (CAAR) algorithm in order to reduce color aliasing artifacts. The CAAR algorithm computes the L level wavelet transform for the demosaiced color planes R, G and B. Thereafter, the CAAR algorithm estimates the correct color value at each pixel location for the colors not associated with that pixel location. For example, to determine the green value at red pixel locations, the CAAR algorithm performs an inverse wavelet transform using the green approximation signal and the red detail signals. This process is repeated for each of the colors (e.g., green values at blue pixel locations, red values at green pixel locations, etc.). In addition, the CAAR algorithm performs an inverse wavelet transform on each of the color planes themselves, so that the pixel values of the color associated with each pixel location are not altered. Thereafter, the inverse wavelet transform of each color plane is combined with the inverse wavelet transform of each of the estimated color values for that color plane to produce correlated R, G and B color planes. It is these correlated R, G and B color planes that may later be compressed using a wavelet-based image compression method, such as the JPEG 2000 standard.