258 results about "Color plane" patented technology

An imaging system (FIG. 3) is disclosed that has a wavelength dependent focal shift caused by longitudinal chromatic aberration in a lens assembly (203) that provides extended depth of field imaging due to focal shift (213,214) and increased resolution due to reduced lens system magnification. In use, multiple wavelengths of quasi-monochromatic illumination, from different wavelength LEDs (206,207) or the like, illuminate the target, either sequentially, or in parallel in conjunction with an imager (200) with wavelength selective (colored) filters. Images are captured with different wavelengths of illumination that have different focus positions (208,209), either sequentially or by processing the color planes of a color imager separately. Extended depth of field, plus high resolution are achieved. Additionally, information about the range to the target can be determined by analyzing the degree of focus of the various colored images.

An adaptive demosaicing method interpolates images based on color edge detection and neighborhood voting. The adaptive demosaicing algorithm uses a voting scheme to determine the direction of interpolation at each missing luminance pixel location. Each color plane votes either horizontal or vertical based on a comparison between the horizontal and vertical components of the degree of change (i.e., gradient, Laplacian or other measure of the degree of change) in that color plane. Votes are counted from the neighborhood pixels as well as from measurements taken at the pixel location itself. Once the luminance plane is fully interpolated, the chrominance planes are filled in by simple bilinear or median interpolation of difference chrominance values. Enhancements to the adaptive demosaicing algorithm permit adaptive smoothing and sharpening.

A bad pixel correction (BPC) algorithm that can be implemented on the image sensor chip is provided for detecting and correcting defective pixels in a digital color image sensor. Gradients of neighboring pixels in at least one other color plane than the color plane of a current pixel and a range of sensor values from neighboring pixels in the same color plane as the current pixel are determined. If the sensor value of the current pixel is outside of the range by a threshold amount that is calculated using one or more of the gradients, the current pixel is determined to be a defective pixel, and replaced using the sensor values of the neighboring pixels in the same color plane.

A raw image consists of a plurality of pixels in a plurality of color planes giving a color image. The raw image is split into at least a low frequency image and a high frequency image. At least the low frequency image is color corrected to produce a color corrected low frequency image. The color corrected low frequency image is combined with at least the high frequency image to give a final image which is of comparable resolution to the raw image but is color corrected.

A lossless image encoding / decoding method and apparatus. The lossless color image encoding apparatus includes a motion prediction image generator estimating a motion between a previous image and a current image and outputting a corresponding prediction image, a residue generator generating a temporal residue corresponding to a difference between a prediction image generated by the motion prediction image generator and the corresponding block of the current image with respect to different components of the color image, a prediction residue generator generating prediction residues by defining a linear-transformed value of one residue among the different components of the color image output from the residue generator as a predictor and using differences between each of the residues of the other components and the predictor, and an entropy encoder receiving the residue forming the predictor from the residue generator and the prediction residues from the prediction residue generator and generating a bitstream. Encoding methods, decoding apparatuses, and decoding methods can be implemented similarly.

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.

An image processing system and demosaicing method are provided to calculate estimated missing color sensor values in an image using a linear prediction from the raw color sensor value at the current pixel location. The raw image is divided into regions of sensor values, and the linear relations between color planes for each region are determined by a regression method that calculates the degree to which different color planes co-vary within each region The missing color sensor values per region are calculated as a scaled and shifted version of the raw color sensor values using linear regression coefficients determined from the local linear regression process.