# A Variety of Choice Methods for Image-Based
## 1 Introduction  
Automatic image colorization is one of the popular research topics in image processing. In general, image colorization is the process of adding colors to mono-chroma images to make them more aesthetically appealing and perceptually meaningful. However, the most crucial task in this paper is how to design a multiple-choice method depending on personal preference to define an appropriate color from the reference images for propagating to the target image. Among deep learning techniques, the convolutional neural networks (CNN) have been validated to be quite useful for image analysis and applications [1; 2; 3]. For example, CNN-based art style transfers have recently been employed to produce impressive artwork [4; 5; 6; 7] to such an extent that a new market has been created for the mobile apps that can stylize user-provided images with a particular art style. The popularity of commercial apps such as Prisma [8] and Deepart.io [9] have been apparently increased. Neural style transfer (NST) is an optimization technique used to take two images--a content image C and a style reference image S (such as an artwork by a famous painter)--and blend them together so the output image looks like the content image, but \"painted\" in the style of the style reference image. In general, there are two main categories of style-transfer algorithms, namely Image-Optimization-Based Online Neural Methods (IOB-NST)and Model-Optimization-Based Offline Neural Methods (MOB-NST) [10]. According to the development process, there are two major approaches in the generative neural method, that is, Fast Style Transfer (FST) [4] and Multi-Style Transfer (MST) [4]. During the training phase in art education and industrial applications, the MST is advantageous in effectively and efficiently producing a variety of styles for the specified image. Further, [PERSON] et al. [11] proposed the seminal work that transfers an arbitrary visual style to content images, achieving so-called universal style transfer (UST). The main point of this paper is completely independent of the image style during the training phase and aims to make it \"style learning-free\". Recent, [PERSON] et al. [12] proposed a new knowledge distillation method to reduce the model size, which exploits the phenomenon that encoder-decoder pairs in UST construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models. Although the artistic effect of the NST-based methods is impressive, there are three issues worth noting. The first is the \(S_{C}\) image copies the color distribution of the S image which might be undesirable for most artwork applications. In order to tackle this problem, one possible method is to use luminance-only transfer [13] by a 3\(\times\)3 kernel to convert the C and S images from the RGB color space to the YIQ space, respectively. In the inverse conversion, the I and Q components of the C image are retained and the Y component of the C image is replaced by that of the \(S_{C}\) image. Consequently, the color information of the C image can be preserved in the new image while the spatial details of the new image are inherited from the \(S_{C}\) image. Second, in the control of the trade-off between content and style in the \(S_{C}\) image, the predefined relative content-style weight plays a leading role. If the stylized effect is not as expected, one solution is to use the blending technique to adjust the mixing ratio between C and \(S_{C}\) images [14]. Therefore, retraining is not required at all. The third concern is the use of batch or instance normalization in FST and MST. It is particularly useful for forwarding propagation when the number of CNN layers is too large. However, the normalization may significantly degrade the image contrast performance. To improve the visual effect, some effective contrast-enhancing methods have been proposed [15; 16]. [PERSON] et al. [15] present intuitive ways to control NST.