scholarly journals Auto Colorization of Gray-Scale Image Using YCbCr Color Space

2020 ◽  
pp. 3379-3386
Author(s):  
Eman Saleem ◽  
Nidhal K. El Abbadi
Keyword(s):  
Color Image ◽  
Color Space ◽  
Quality Measures ◽  
One Dimension ◽  
Reference Image ◽  
Three Dimension ◽  
Gray Image ◽  
Ycbcr Color Space ◽  
True Colors

The process of converting gray images or videos to color ones by adding colors to them and transforming them from one-dimension to three-dimension is called colorization. This process is often used to make the image appear more visually appealing. The main problem with the colorization process is the lack of knowledge of the true colors of the objects in the picture when it is captured. For that, there is no a unique solution. In the current work, the colorization of gray images is proposed based on the utilization of the YCbCr color space. Reference image (color image) is selected for transferring the color to a gray image. Both color and gray images are transferred to YCbCr color space. Then, the Y value of the gray image is combined with the Cb and Cr values of the reference image, based on the Euclidian distance between them. The quality of the resulted image was measured based on several quality measures, which indicated very good results. The proposed algorithm is simple, efficient, and fast.

scholarly journals Automatic gray images colorization based on lab color space

2020 ◽  
Vol 18 (3) ◽  
pp. 1501
Author(s):  
Nidhal K. El Abbadi ◽  
Eman Saleem Razaq
Keyword(s):  
Color Image ◽  
Color Space ◽  
Accurate Method ◽  
Reference Image ◽  
The Public ◽  
Gray Image ◽  
Black And White ◽  
Manual Intervention ◽  
Lab Color Space ◽  
Fully Automatic

<p>The colorization aim to transform a black and white image to a color image. This is a very hard  issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image.  The reference image converted to  Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concatenate with both a, and b channels before converting to RGB image. The results were promised compared with other methods.</p>

Flame Detection Using Generic Color Model and Improved Block-Based PCA in Active Infrared Camera

2018 ◽  
Vol 32 (05) ◽  
pp. 1850014
Author(s):  
Qian Zhao ◽  
Fengdong Sun ◽  
Wenhui Li ◽  
Peixun Liu
Keyword(s):  
Color Image ◽  
Color Space ◽  
Infrared Image ◽  
Infrared Camera ◽  
Detection Algorithm ◽  
Dynamic Features ◽  
Ycbcr Color Space ◽  
Imaging Results ◽  
Flame Detection ◽  
Block Based

In this paper, we proposed an all-weather flame detection algorithm which could make full use of active infrared cameras presently installed in many public places for surveillance purposes. Firstly, according to the different spectral imaging results in day and night, we propose a video type classification algorithm (VTCA) via imaging clues. VTCA could help us select different flame visual features in color image and infrared image. Secondly, we use a generic YCbCr-color-space-based chrominance model to extract regions of interest (ROI) of flame. Thirdly, two flame dynamic features are used to verify the candidate ROIs, which are common flame flicker feature and an improved block-based PCA in consecutive frames. The experimental results show that the proposed flame detection model has been successfully applied to various situations, including day and night, indoor and outdoor on our test video datasets, and it gives a better performance compared with other state-of-the-art methods.

Color Image Encryption Based on the Multiple-Order Discrete Fractional Cosine Transform and Chaos in YCbCr Space

2012 ◽  
Vol 182-183 ◽  
pp. 1839-1843
Author(s):  
Xian Zhe Luo ◽  
Nan Run Zhou ◽  
Qing Min Zhao ◽  
Jian Hua Wu
Keyword(s):  
Image Encryption ◽  
Color Image ◽  
Color Space ◽  
Human Vision ◽  
Transmission Channel ◽  
Color Image Encryption ◽  
Cosine Transform ◽  
Ycbcr Color Space ◽  
Pixel Value ◽  
Encryption Method

Based on the theory that a color image can be decomposed into three primary components and each one can be seen as a gray image, we propose a color image encryption method with multiple-order discrete fractional cosine transform (MODFrCT), which is a kind of encryption with the secrecy of pixel value and pixel position simultaneously. The complex number mode that has a real part and an imaginary one is used in this encryption method to save the transmission channel. Human vision is more sensitive to the Y component than to other two components in YCbCr color space and this color format is used for encrypting the color image. Chaos is introduced to scramble the image phases both in spatial and transformation domains. The numerical simulations demonstrate the validity and efficiency of this scheme and the robustness of the method against occlusion attack is examined.

Color Medical Image Enhancement Using Y-H Model and Wavelet Homomorphic Filtering

2013 ◽  
Vol 411-414 ◽  
pp. 1020-1024
Author(s):  
Hua Liang ◽  
Zhen Tao Zhou ◽  
Hao Feng ◽  
Li Jun Ding ◽  
Ju Ping Gu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Color Image ◽  
Color Space ◽  
Inverse Transformation ◽  
Homomorphic Filtering ◽  
Enhancement Method ◽  
Rgb Color Space ◽  
Medical Image Enhancement

Color medical images are widely used in the field of medical diagnosis. Image enhancement is one of the most important pretreatment methods which can enhance the quality of images. In this paper, a novel color image enhancement method using Y-H model and wavelet homomorhpic filtering is put forward. The chromaticity numbers matrix and intensity numbers matrix of color images are get using Young-Helmholtz (YH) transform. The chromaticity numbers matrix remains unchanged. Wavelet homomorphic filtering method is used to process intensity numbers matrix . The enhanced intensity numbers matrix and formerly chromaticity numbers matrix are processed by Y-H inverse transformation and disply in RGB color space. The method put forward in the paper is successfully used in color medical image enhancement. Experimental results show that the method have characteristics of nondistortion, better visual effect.

Image Segmentation Utilizing Color-Space Feature

2015 ◽  
Vol 6 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Mohammad A. Al-Jarrah
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Space ◽  
Color Image Segmentation ◽  
Blue Color ◽  
Space Correlation ◽  
Gray Image ◽  
Segmentation Methods ◽  
Third Stage ◽  
Thresholding Algorithm

In this paper, the authors introduced a stochastic model for color images. Utilizing this model, they proposed a new method for color image segmentation. The proposed method consists of three stages; the first stage considers the red, green, and blue color component of the image as a gray image. One of the known gray image Thresholding algorithm is applied on the three color components. The second stage segments the image based on the results of first stage. This stage produces eight color segments. The third stage identifies the segments through color-space correlation. Color-space correlation algorithm assumes that a set of pixels are considered to belong to one region if and only if they belong to the same color cluster and all connected using neighborhood filters. The last stage may produce very small segments. These small segments are merged with their closed neighbors based on color features. Finally, Conducted experiments achieved perceptually accepted segments and compare favorably to other segmentation methods.

scholarly journals MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

2020 ◽  
Vol 10 (1) ◽  
pp. 196
Author(s):  
Varshali Jaiswal ◽  
Varsha Sharma ◽  
Sunita Varma
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Color Image ◽  
Color Space ◽  
Region Growing ◽  
Two Dimensions ◽  
Color Image Segmentation ◽  
Three Dimensions ◽  
One Dimension ◽  
Moth Flame Optimization Algorithm

<span lang="EN-US">Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process.</span>

scholarly journals Chroma Enhancement in CIELAB Color Space Using a Lookup Table

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 32
Author(s):  
Tadahiro Azetsu ◽  
Noriaki Suetake
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Lookup Table ◽  
Color Gamut ◽  
Human Visual Perception ◽  
Cielab Color Space ◽  
Rgb Color Space ◽  
Cielab Color

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

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