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.

Enhanced sensitivity color video camera with reduced red component in second luminance signal

Abstract: A color video camera uses an infrared cut-off filter, a complementary filter and a solid state image pick-up device. The infrared cut-off filter cuts off near infrared light of image above a wavelength λc, which is set in the range of 670 nm≦λc≦780 nm. A color sampling/generating circuit has a matrix circuit connected to the solid state image pick-up device for providing a first luminance signal, a second luminance signal, a red channel signal and a blue channel signal. The second luminance signal is used for producing a red color difference signal and a blue color difference signal by using the red channel signal and the blue channel signal in a processing circuit. In the matrix circuit, matrix coefficients for producing the second luminance signal are set to reduce the red component of the second luminance signal. Further, the red color difference signal below a predetermined value is extracted and added to the first luminance signal to reduce the red component of the first luminance signal.

GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex

Abstract: Retinex is an early and famous theory attempting to estimate the human color sensation derived from an observed scene. When applied to a digital image, the original implementation of retinex estimates the color sensation by modifying the pixels channel intensities with respect to a local reference white, selected from a set of random paths. The spatial search of the local reference white influences the final estimation. The recent algorithm energy-driven termite retinex (ETR), as well as its predecessor termite retinex, has introduced a new path-based image aware sampling scheme, where the paths depend on local visual properties of the input image. Precisely, the ETR paths transit over pixels with high gradient magnitude that have been proved to be important for the formation of color sensation. Such a sampling method enables the visit of image portions effectively relevant to the estimation of the color sensation, while it reduces the analysis of pixels with less essential and/or redundant data, i.e., the flat image regions. While the ETR sampling scheme is very efficacious in detecting image pixels salient for the color sensation, its computational complexity can be a limit. In this paper, we present a novel Gradient-based RAndom Sampling Scheme that inherits from ETR the image aware sampling principles, but has a lower computational complexity, while similar performance. Moreover, the new sampling scheme can be interpreted both as a path-based scanning and a 2D sampling.

An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification

Abstract: We introduce an improved single image haze removal algorithm, which combines dark channel prior (DCP) and histogram specification. First, the dark channel prior knowledge proposed by Kaiming He is analyzed and a conclusion is drawn that the haze removal image based on dark channel prior will have a tendency to dim and indistinct in some specific situations. Especially, when cleaning the haze in the image with large background area and low contrast, DCP result appears obvious anamorphose. Next, in order to improve the dehazing result of this kind of image, we propose an approach to change the contrast and intensity of haze removal image after DCP method by rebuilding the histogram of the image. Then, a modified approach is applied to fit general haze image. We experiment our method with a variety of outdoor haze images. The effectiveness of our method is demonstrated in comparison with DCP result when the input image contains low contrast scene and large background area, such as thick fog or dark surroundings in dusk. Our job makes up the deficiency of the dark channel model for this kind of image and enhance the contrast of the scene. Furthermore, the experimental results show that the dehazing effect on general haze image appears more close to real scene than dark channel model.

Blood Vessel Extraction for retinal images using morphological operator and KCN clustering

Abstract: This paper presents an automated blood vessel detection method from the fundus image. The method first performs some basic image preprocessing tasks on the green channel of the retinal image. A combination of morphological operations like top- hat and bottom-hat transformations are applied on the preprocessed image to highlight the blood vessels. Finally, the Kohonen Clustering Network is applied to cluster the input image into two clusters namely vessel and non-vessel. The performance of the proposed method is tested by applying it on retinal images from Digital Retinal Images for Vessel Extraction (DRIVE)database. The results obtained from the proposed method are compared with three other state of the art methods. The sensitivity, false-positive fraction (FPF) and accuracy of the proposed method is found to be higher than the other methods which imply that the proposed method is more efficient and accurate.