Here we are presenting some novel techniques for squirting colors in grayscale images. The problem of coloring grayscale images has no exact solution. Here we are attempting to minimize the human efforts needed in manually coloring the grayscale images. We need human interaction only to find a reference color image, then the job of transferring color traits from reference color image to grayscale image is done by proposed techniques. In these techniques, the color palette is prepared using pixel windows of some degrees taken from reference color image. Then the grayscale image is divided into pixel windows with same degrees. For every window of grayscale image the palette is searched for equivalent color values, which could be used to color grayscale window. In the whole process the luminance values of reference color image and target grayscale image are only matched and based on best possible match the respective chromaticity values of color image are transferred to grayscale image. For palette preparation first we used RGB color space and then Kekre's LUV color space[9]. Results with Kekre's LUV color space were comparatively better. To improve the searching time through color palette the exhaustive and Kekre's fast search are used.

Color Traits Transfer to Grayscale Images

A color-transfer method that can transfer colors of an image to another for the local regions using their dominant colors is proposed. In order to naturally transfer the colors and moods of a target image to a source image, we need to find the local regions of colors that need to be modified in the image. Since the dominant colors of each image can be used for the estimation of color regions, we develop a grid-based mode detection, which can efficiently estimate dominant colors of an image. Based on these dominant colors, our proposed method performs a consistent segmentation of source and target images by using the cost-volume filtering. Through the segmentation procedure, we can estimate complex color characteristics and transfer colors to the local regions of an image. For an intuitive and natural color transfer, region matching is also crucial. Therefore, we use the visually prominent colors in the image to meaningfully connect each segmented region in which modified color-transfer method is applied to balance the overall luminance of the final result. In the Experimental Result section, various convincing results are demonstrated through the proposed method.

Local color transfer between images using dominant colors

In current colorization techniques, major user intervention is required in the form of tedious, time-consuming scribble drawing. Moreover, color leakage usually occurs across contours and object boundaries. In this paper, we focus on automatic scribble generation and structure-preservation mechanism, which are still open issues of colorization. Firstly, we generate scribbles automatically along points where the spatial distribution entropy achieves locally extreme value. Given the color scribbles, we compute quaternion wavelet phases to conduct colorization along equal-phase lines. These lines across scribbles and monochrome patches locate textures with similar pattern distribution. Contour 'strength' model is also established in scale space to direct color propagation among similar edge structures. Finally, we reconstruct color image patches as vector elements using polar representation in quaternion algebra, well-preserving interrelationship between color channels. The experimental results demonstrate that the proposed colorization method can achieve natural color transitions between different objects with automatically generated scribbles.

Colorization using quaternion algebra with automatic scribble generation

This paper proposes a new color image coding scheme called "colorization image coding." The scheme is based on the colorization technique which can colorize a monochrome image by giving a small number of color pixels. We develop algorithms useful for color image coding. First, the luminance component is separated from an input color image. Then, a small number of color seeds are selected as chrominance information. The luminance image component is coded by a lossy coding technique and the chrominance image component is stored as color seeds. The decoding is performed by the colorization algorithm. It is shown that this colorization technique is effective to image coding, especially for high compression rate, through the experiments using different types of images.

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Color image coding by colorization approach

Colorization is a term used to describe a computerized process for adding color to black and white pictures, movies or TV programs. This process replaces a scalar value that represents pixelspsila intensity with a vector in a three dimensional color space composed of hue, saturation and luminance, and since this mapping between intensity and color has no inherently rdquocorrectrdquo solution, human interaction and/or external information usually plays a large role. In this paper we present a novel colorization method that takes advantage of the modified morphological distance transform to automatically propagate the color scribbled by a user on the gray scale image. The introduced modification of the distance transform is based on the Gibbs distribution, which governs the behavior of a virtual particle performing a random walk on the image domain. Such a modification allows for the application of the distance transform to the gray scale images and yields high quality colorization results.

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Digital image colorization based on probabilistic distance transformation

We develop an effective algorithm for colorizing a grayscale image In our approach, a reference color image, an RGB to color transform (=luminance, =chrominance), and a block-based vector quantization of luminance mapping (VQLM) technique are used to automatically colorize the grayscale image The VQLM technique compares the grayscale image with the luminance of the color reference image to obtain the information of the planes of the grayscale image After the chrominance is padded, the inverse color transform, to RGB, colorizes the grayscale scene is colorized Meanwhile, we create a mean of VQLM (MVQLM) method to improve the quality of the colorized grayscale image Experimental results show the MVQLM method is better than the VQLM method Also, we investigate colorizing the grayscale image working in the and YIQ spaces The simulation results also reveal that working in the space is slightly better than working in the YIQ space Compared to other colorizing schemes, our proposed method has two advantages: 1 the codebook and MVQLM techniques colorize the grayscale images for any size image that can be evenly divided by 2 automatically; and 2 the MVQLM method obtains a smoother colorizing effect and improved quality compared to the VQLM method

Chin-Ho Chung

Papers

Transferring color to grayscale images using vector quantization of luminance mapping techniques