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.

Weighted gradient based and color corrected interpolation

Abstract: The apparatus and method invented are operating upon a digital image signal obtained from an image sensor. The sensor is covered with different colored filters and is only able to record the color transmitted through each specific filter into the photosite or pixel. This type of sensor is known as a color filter array or CFA sensor. The different colored filters are arranged in a predefined pattern across the sensor. To obtain a full color image the missing color information is estimated by a set of weighed values obtained by an inverted gradient function. The set of weighted values is found from the neighboring pixels in the four compass directions, north, east, west and south or is found horizontally and vertically. The surrounding pixels are corrected by the chrominance channel to better fit the center pixel in the luminance channel, prior to using the gradient functions. The chrominance channel is interpolated in a similar manner and is also corrected to better fit the center pixel before the inverted gradient functions are applied.

Image colorization using sparse representation

Abstract: Image colorization is the task to color a grayscale image with limited color cues. In this work, we present a novel method to perform image colorization using sparse representation. Our method first trains an over-complete dictionary in YUV color space. Then taking a grayscale image and a small subset of color pixels as inputs, our method colorizes overlapping image patches via sparse representation; it is achieved by seeking sparse representations of patches that are consistent with both the grayscale image and the color pixels. After that, we aggregate the colorized patches with weights to get an intermediate result. This process iterates until the image is properly colorized. Experimental results show that our method leads to high-quality colorizations with small number of given color pixels. To demonstrate one of the applications of the proposed method, we apply it to transfer the color of one image onto another to obtain a visually pleasing image.

Fast Lossless Depth Image Compression

Abstract: A lossless image compression technique for 16-bit single channel images typical of depth cameras such as Microsoft Kinect is presented. The proposed "RVL" algorithm achieves similar or better compression rates as existing lossless techniques, yet is much faster. Furthermore, the algorithm's implementation can be very simple; a prototype implementation of less than one hundred lines of C is provided. The algorithm's balance of speed and compression make it especially useful in interactive applications of multiple depth cameras on local area networks. RVL is compared to a variety of existing lossless techniques, and demonstrated in a network of eight Kinect v2 cameras.