Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Karaoke control system for a plurality of karaoke devices

Abstract: When images of the image genre most suitable for the selected song are being reproduced (Yes in S14), the karaoke terminal picks up those image data from a channel over which they are transmitted. If images of that image genre are not being reproduced (No in S14), when the number of players presently playing is less than four (No in S19), a player that is not presently playing starts playing a video disk to reproduce images of that image genre in S20. When the number of players presently playing is equal to or greater than four, according to the image genre D and/or H reproducing status, a player that is not presently playing starts playing a video disk to reproduce images of image genre D or H that includes or is common to the most suitable image genre.

Image sensor with improved lightsensitivity

Abstract: An image sensor for capturing a color image is disclosed having a two-dimensional array having first and second groups of pixels wherein pixels from the first group of pixels have narrower spectral photoresponses than pixels from the second group of pixels and wherein the first group of pixels has individual pixels that have spectral photoresponses that correspond to a set of at least two colors. Further, the placement of the first and second groups of pixels defines a pattern that has a minimal repeating unit including at least twelve pixels. The minimal repeating unit has a plurality of cells wherein each cell has at least two pixels representing a specific color selected from the first group of pixels and a plurality of pixels selected from the second group of pixels (panchromatic = white pixels) arranged to permit the reproduction of a captured color image under different lighting conditions .

Color Correction by Estimation of Dominant Chromaticity in Multi-Scaled Retinex

Abstract: In image capture a scene with nonuniform illumination has an influence on the image quality, especially the contrast and detail in dark regions. Generally, the tone curve or histogram of an image is modified to improve the contrast and detail, yet this is in- sufficient as the intensity and chromaticity of the illumination vary with geometric position. Thus, the multi-scaled retinex algorithm has been proposed, where the influence of nonuniform illumination is reduced by partitioning the original image using local average im- ages that are estimated based on Gaussian filtering of the original image. However, the multi-scaled retinex algorithm produces color distortion as the local average images are independently estimated for each channel. In particular, if the chromatic distribution of the original image is not uniform and is dominated by a certain chroma- ticity, the local average image includes not only the intensity and chromaticity of the illumination but also the dominant chromaticity through the Gaussian filtering, thereby distorting the color. Accord- ingly, this article proposes a multi-scaled retinex using a modified local average image to reduce the color distortion by the dominant chromaticity of the original image. As with the multi-scaled retinex algorithm, the local average image is obtained through Gaussian filtering of the original image. The local average image is then di- vided by the average chromaticity value of the original image to reduce the influence of the dominant chromaticity. However, be- cause the average chromaticity value includes the dominant chro- maticity of the original image and the chromaticity of the illumination, the chromaticity removed from the illumination in the local average image needs to be compensated. Therefore, the chromaticity of the illumination is estimated based on the chromaticity of the highlight regions in the original image. The chromaticity of the local average image is then modified by the estimated chromaticity. In experi- ments, the proposed method was found to improve local contrast and reduce the color distortion. © 2009 Society for Imaging Science and Technology. DOI: 10.2352/J.ImagingSci.Technol.2009.53.5.050502

A dehazing algorithm using dark channel prior and contrast enhancement

Abstract: This paper proposes a dehazing algorithm based on dark channel prior and contrast enhancement approaches. The conventional dark channel prior method removes haze and thus restores colors of objects in the scene, but it does not consider the enhancement of image contrast. On the contrary, the image contrast method improves the local contrast of objects, but the colors are often distorted due to the over-stretching of contrast. The proposed algorithm combines the advantages of these two conventional approaches for keeping the color while dehazing. For this, an optimization function is proposed to balance between the contrast and colors distortion, where the contrast measure follows the conventional image statistics and the hue component is used to constrain the color changes. According to the experimental results, the proposed approach compensates for the disadvantages of conventional methods, and enhances contrast with less color distortion.