Abstract: Automatic white balancing of a digital image. In one example embodiment, a method for automatic white balancing of a full-color input image includes several acts. First, pixels of the input image are selected according to one or more predetermined criteria. Next, global gain values for R and B components of the input image are determined using the selected pixels. Then, local gain values for the R and B components of each individual pixel of the input image are determined based on characteristics of each individual pixel. Next, a final gain value for the R and B components of each individual pixel of the input image is determined based on contributions of the local and global gain values. Finally, a white-balanced output image is produced by adjusting the R and B components of each pixel in the input image using the corresponding final gain value.

Abstract: Automatically resizing demosaicked full-color images using edge-orientation maps formed in the demosaicking process. In a first example embodiment, a method for automatic upsampling of a demosaicked image includes several acts. First, a demosaicked image and an edge-orientation map that was created during the creation of the demosaicked image are received. Next, pixels of the demosaicked image are filled into an upsampled image. Then, edge-orientation values of pixels of the edge-orientation map are filled into an upsampled edge-orientation map. Next, an interpolation direction is determined for each pixel in which upsampling of the demosaicked image should be performed using the upsampled edge-orientation map. Finally, missing pixels in the upsampled image are estimated by performing interpolation along the interpolation direction using available pixels surrounding each missing pixel location.

Abstract: Joint automatic demosaicking and white balancing. In one example embodiment, a digital image processing method includes several acts. First, directional color correlation signals, global gains, and orientations of edges are calculated in a CFA input image. Next, missing luminance components in CFA chrominance locations are demosaicked along edges in the input image using CFA chrominance components and the directional color correlation signals. Then, the CFA chrominance components are white-balanced using the demosaicked luminance components, the CFA chrominance components, and white-balancing gains expressed as a function of the global gains and local gains calculated directly from a pixel under consideration. Next, missing chrominance components in CFA chrominance locations in the input image are demosaicked. Finally, missing chrominance components in CFA luminance locations in the input image are demosaicked.

Abstract: Disclosed are methods, devices, and computer program products for red-eye detection in a digital image. In one example embodiment, a method for detecting a red-eye effect in a digital image includes several acts. First, red pixels having a predetermined degree of redness are identified in the image. Next, redness contrast is detected with respect to each of the red pixels and redness is then enhanced for those red pixels having a predetermined level of redness contrast. The pixels identified as being red are then further refined by applying another redness threshold based on one or more color characteristics associated with the red pixels. The refined set of red pixels may then be partitioned into a set of one or more candidate red-eye objects. A candidate red-eye object may be removed as a false positive based on geometric constraints associated with red-eye objects and/or proximity of the object to pixels with human skin-like color tones.

Abstract: Disclosed are methods, devices, and computer program products for red-eye detection in an image. In one example embodiment, a method for detecting red-eye objects in an image includes several acts. First, a set of candidate red-eye objects identified in the image is received. Then, features are extracted from the candidate red-eye objects and, with a plurality of classifiers, a false red-eye object is eliminated from the set of candidate red-eye objects based on the extracted features. First and second ones of the plurality of classifiers are optimized for classifying objects in a first range of sizes using first and second ones of the extracted features, respectively. Furthermore, third and fourth ones of the plurality of classifiers are also optimized for classifying objects using the first and second ones of the extracted features, respectively, but for objects in a second range of sizes.

Abstract: An image processing apparatus that receives mosaic image data having settings of only one color component, R, G, or B, in each pixel and subjects the received mosaic image data to a series of image processing to generate color image data with settings of all the three color components, R, G, and B, in each pixel; wherein the mosaic image data has the form of a Bayer color filter array; and the image processing apparatus includes: a vertical-direction color difference component computation module; a horizontal-direction color difference component computation module; an edge direction determination module; a color component interpolation module; an oblique edge pixel detection module; an oblique edge direction determination module; and an oblique edge pixel interpolation correction module.

Abstract: An image processing procedure receives mosaic image data and calculates vertical and horizontal-direction color difference components for each pixel. The image processing procedure subsequently selects an R pixel or a B pixel from the mosaic image data, and compares a variation of the vertical-direction color difference component with a variation of the horizontal-direction color difference component with regard to each of at least the selected pixels to detect edge directions of the at least selected pixels. The edge directions thus obtained are collected in an edge direction map, and then the edge directions are compared with the surrounding edge directions to remove edge noise in advance. The image processing procedure refers to the detected edge directions, and interpolates a missing color component in each pixel of the mosaic image data with the settings of one color component in each pixel in the mosaic image data.

Abstract: An image processing method that demosaicks a mosaic input image of R, G, and B components to generate a full color output image. The image processing method calculates both vertical and horizontal luminance-chrominance difference components for each pixel of the mosaic input image. Next, the image processing method calculates an enhanced version of both the vertical and horizontal luminance-chrominance difference components for each pixel of the mosaic input image. Next, the image processing method evaluates the variations in the enhanced luminance-chrominance difference components in order to create an edge directional map indicating the direction in which demosaicking should be performed. Then, the image processing method interpolates a G component for each of the pixels with the original R and B components using the edge directional map.

Abstract: The image processing procedure of the invention receives mosaic image data and calculates a vertical-direction color difference component with regard to each of pixel columns in the mosaic image data in a vertical direction and a horizontal-direction color difference component with regard to each of pixel rows in the mosaic image data in a horizontal direction.

Abstract: An image processing method that demosaicks a mosaic input image to generate a full color output image. The image processing method calculates both vertical and horizontal luminance-chrominance difference components for each pixel of the mosaic input image. Next, the image processing method calculates an enhanced version of both vertical and horizontal luminance-chrominance difference components for each pixel of the mosaic input image. Then, the image processing method interpolates a G component for each of the original R and B components. Next, the image processing method detects a signal overshoot or undershoot in each interpolated G component and to clamps each interpolated G component with a detected signal overshoot or undershoot to the closest neighboring original G component. Next, the image processing method interpolates missing R and/or B components in each pixel location of the captured image.

Abstract: The image processing procedure of the invention receives mosaic image data and calculates a vertical-direction color difference component with regard to each of pixel columns in the mosaic image data in a vertical direction and a horizontal-direction color difference component with regard to each of pixel rows in the mosaic image data in a horizontal direction. The mosaic image data is expressed by a combination of pixel columns with alternate arrangement of pixels of a G component and pixels of an R component in the vertical direction, pixel columns with alternate arrangement of pixels of the G component and pixels of a B component in the vertical direction, pixel rows with alternate arrangement of pixels of the G component and pixels of the R component in the horizontal direction, and pixel rows with alternate arrangement of pixels of the G component and pixels of the B component in the horizontal direction.

Abstract: Enlarging a digital image. In one example embodiment, a method for enlarging a digital image includes various acts. First, an enlargement factor ? is selected for an input image. Next, a pixel in the input image is selected. Then, a supporting window is placed over the input image. Next, a ?×? block of output pixels is produced. Each pixel in the block of output pixels is produced using a set of ?2 distinct weight matrices. Then, the block of output pixels is assembled into an output image. Next, the acts of selecting a pixel, placing the supporting window, producing a block of output pixels, and assembling the block of output pixels into the output image are repeated for each of the remaining pixels in the input image.

Abstract: Automatic white balancing of a digital image. In one example embodiment, a method for automatic white balancing of a full-color input image includes several acts. First, pixels of the input image are selected according to one or more predetermined criteria. Next, global gain values for R and B components of the input image are determined using the selected pixels. Then, local gain values for the R and B components of each individual pixel of the input image are determined based on characteristics of each individual pixel. Next, a final gain value for the R and B components of each individual pixel of the input image is determined based on contributions of the local and global gain values. Finally, a white-balanced output image is produced by adjusting the R and B components of each pixel in the input image using the corresponding final gain value.