Objective

2

Image colorization is the process of assigning color information to grayscale images and retains grayscale image texture information. The aim of colorization is to increase the visual appeal of an image. This technology is widely used in many areas

such as medical image illustrations

remote sensing images

and old black-and-white photos. Colorization methods have to main categories

namely

user-assisted and automatic colorization methods. User-assisted colorization methods require users to manually define a layer mask or mark color scribbles on a grayscale image. This method is time-consuming and cannot provide sufficient and desirable color scribbles. Automatic colorization methods can reduce user effort and transfer color from a sample color image. The color image is called the reference/source image

and the grayscale image to be colorized is called the target image. The primary difficulty of these automatic colorization methods is to accurately transfer colors and satisfy spatial consistency. Most of these approaches achieve spatial coherency by using weighted filter or global optimization algorithms during colorizing. However

these methods may result in oversmoothed colorization or blur color in edge regions.

Method

2

We use a grayscale image colorization approach based on a local adaptive weighted average filter. This proposed method considers local neighborhood pixel information and automatically adjusts the domain pixel weights to ensure correct color migration and clear boundary results. A reference image with similar contents as the target image is provided to achieve color transfer. The method includes the following steps:First

the class probability distribution and classification are obtained. Support vector machine (SVM) is adopted to calculate class probability based on feature descriptors

mean luminance

entropy

variance

and local binary pattern (LBP) and Gabor features. The probability results and classification are post-processed to enhance the spatial coherency combined with superpixels that are extracted based on improved simple linear iterative clustering (ISLIC). Second

the color candidate in the reference image is determined based on matching low-level features in the corresponding class. Thereafter

each pixel with high-confidence class probability is assigned a color from the candidate pixel by using an adaptive weight filter. The adaptive weight

which is defined by the class probability of small neighborhood pixels around the corresponding pixel

can improve local spatial consistency and avoid confusion colorization in the boundary region. Finally

the optimization-based colorization algorithm is used on the remaining unassigned pixels with low-confidence class probability.

Result

2

This paper analyzes single pixel-based

weighted average

and adaptive weighted average methods. Results demonstrate that the adaptive weighted average method is better than the other strategies. The colorized images illustrate that our method takes advantage of the other strategies and that it not only has high spatial consistence but also ensures the boundary detail information with high color discrimination. Compared with previous colorization methods

our method works well in colorization. Colorized images achieved by Gupta's method and Irony's method have obvious erroneous colors due to inaccurate matching or corresponding pixels. Charpiat's method produce oversmoothed color on the boundary regions. Images colorized by using Zhang's method are extracted by training more than a million color images and diversities of colors based on CNN. However

some unreliable and undistinguished colors appear on the boundary contours. The colorization results obtained by using a local adaptive weighted average filter ensures the correctness of color transfer and spatial consistence and avoids oversmoothing simultaneously in the edge areas. Thus

our proposed method performs better than the existing methods. The evaluation scores for experimental images using our method are higher than 3.5

especially in local areas

and the evaluation results are close to or higher than 4.0

which is greater than the results of those using the existing colorization method.

Conclusion

2

A new colorization approach using color reference images is presented in this paper. The proposed method combines SVM and ISLIC to determine the class probabilities and classifications with high spatial coherence for images. The corresponding pixels are matched based on the space features according to the same class label between the reference and the target images. A local adaptive weighted average filter is defined to transfer the chrominance from the source image to the grayscale image with high-confidence pixels to facilitate spatially coherent colorization and avoid oversmoothing. The colorized pixels are considered automatic scribbles to be spread across all pixels by using global optimization to obtain the final colorization result. Experimental results demonstrate that our proposed method can achieve satisfactory results and is competitive with the existing methods. However

several limitations are observed. First

this method is not fully automated

requiring some human intervention to provide class samples during the process of calculating class probability. Second

the selected space features are not optimal for all images

especially for images with complex textures and rich colors. We will focus on fully automatic operation and general features in our future work.