A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.

This study improves the compression efficiency of Lee's colorization-based coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorization-based coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorization-based coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorization-based coding framework first obtains a base and color components by RGB-YCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of base-color components. Furthermore, we embed an edge-preserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGB-YCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional method.

This letter proposes a new image colorization algorithm based on the sparse optimization. Introducing some assumptions, a problem of recovering a color image from a grayscale image with the small number of known color pixels is formulated as a mixed l0/l1 norm minimization, and an iterative reweighted least squares (IRLS) algorithm is proposed. Numerical examples show that the proposed algorithm colorizes the grayscale image efficiently.

Image colorization is a semi-automatic process of adding colors to monochrome images and videos. Using existing methods, required human assistance can be limited to annotating the image with color scribbles or selecting a reference image, from which the colors are transferred to a source image or video sequence. In the work reported here we have explored how to exploit the textural information to improve this process. For every scribbled image we determine the discriminative textural feature domain. After that, the whole image is projected onto the feature space, which makes it possible to estimate textural similarity between any two pixels. For single image colorization based on a set of color scribbles, our contribution lies in using the proposed feature space domain rather than the luminance channel. In case of color transfer used for colorization of video sequences, the feature space is generated based on a reference image, and textural similarity is used to match the pixels between the reference and source images. We have conducted extensive experimental validation which confirmed the importance of using textural information and demonstrated that our method significantly improves colorization result.

Recently, a novel approach to color image compression based on colorization has been presented. The conventional method for colorization-based image coding tends to lose the local oscillation of chrominance components that the original images had. A large number of color assignments is required to restore these oscillations. On the other hand, previous studies suggest that an oscillation of a chrominance component correlates with the oscillation of a corresponding luminance component. In this paper, we propose a new colorization-based image coding method that utilizes the local correlation between texture components of luminance and chrominance. These texture components are obtained by a total variation regularized energy minimization method. The local correlation relationships are approximated by linear functions, and their coefficients are extracted by an optimization method. This key idea enables us to represent the oscillations of chrominance components by using only a few pieces of information. Experimental results showed that our method can restore the local oscillation and code images more efficiently than the conventional method, JPEG, or JPEG2000 at a high compression rate.