Estimation of scribble placement for painting colorization

Scientific Research An Academic Publisher. Overview of Scribbled-Based Colorization. Colorization refers to a process of adding colors to black-and-white images with grayscale informationsketches or even monochrome motion pictures. The traditional hand-coloring methods are very time-consuming and require the effort of a whole group of professional artists.

Therefore, many computer-assisted colorization algorithms have been invented to make the process of colorization more efficient. Computer-assisted colorization can be implemented in a large range of areas. To reduce the cost, computer-assisted colorization algorithms are invented, offering filmmakers a less expensive way of producing technicolor and bringing more realistic visual effects to the audience. As a second example, it takes a lot of efforts to colorize the sketch picture after a painter finishes the creative sketch because painters need to find the proper color first, which is very difficult for the novice painters, and then colorize every region in the picture manually, which is very time-consuming.

According to the participation level of users, computer-assisted colorization methods can be divided into three domains: automatic, semi-automatic and user-guided colorization methods. Automatic colorization methods are recent approaches by which the monochrome pictures are directly colorized by training a Convolutional Neural Networks CNN with a large-scale image collection. Semi-automatic colorization methods denote approaches by which color pattern is transferred from one or more reference images to the input monochrome picture.

User-guided colorization methods are approaches by which users can directly decide color of the corresponding region. In this paper, we will focus on scribble-based colorization in user-guided colorization. First, we will introduce the history of the scribble-based colorization briefly. Then, we will elaborately talk about three areas of scribble-based colorization, including grayscale image colorization, sketch colorization and colorization with networks.

In each area, we will make analyses of differences of principles, methods, performance among the colorization algorithms. Finally, we will draw a conclusion about their advantages and disadvantages.

We hope that these analyses can become a useful resource for the scribble-based colorization and computer graphic community. In the earlier days, the process of colorization was divided into two separated parts: segmentation and filling. However, the error ratio in segmentation remains to be high, which means that a lot of user-interventions are needed to fix the errors, making colorization a tedious, time-consuming and expensive task. To reduce user-interventions, Anat Levin and her colleagues designed a method that focused on assigning colors of pixels utilizing the optimization algorithm according to the similarities of intensities, improving the accuracy in colorization Levin et al.

The non-iterative method combined with the adaptive edge extraction Huang et al. To achieve a better visual effect when user strokes are sparse, Luan et al.

For the same purpose, for the same purpose, Xu et al. However, intensity-based colorization methods used in colorizing grayscale images may fail to colorize sketch because manga has no grayscale information.Colorization is a technique to automatically produce color components for monochrome images and videos based on a few input colors.

Generally, image colorization is initialized from a number of seed pixels whose colors are specified by users, and then the colors are gradually prorogating to the monochrome surroundings under a given optimization constraint. So, the performance of colorization is highly dependent on the selection of seed pixels. However, little attention has been paid to the selection of seed pixels, and how to improve the effectiveness of manual input remains a challenging task.

To address this, an improved colorization method using seed pixel selection is proposed to assist the users in determining which pixels are highly required to be colorized for a high-quality colorized image. Specifically, the gray-scale image is first divided into non-overlapped blocks, and then, for each block, two pixels that approximate the average luminance of block are selected as the seeds.

After the seed pixels are colored by users, an optimization that minimizes the difference between the seeds and their adjacent pixels is employed to propagate the colors to the other pixels.

The experimental results demonstrate that, for a given amount of inputs, the proposed method can achieve a higher PSNR than the conventional colorization methods. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. An X, Pellacini F Appprop: all-pairs appearance-space edit propagation. ACM Trans Graph 27 3 — Balinsky A, Mohammad N Sparse natural image statistics and their applications to colorization and compression.

In: International Conference on Image Processing. Trans Image Process 23 1 — Chaumont M, Puech W Attack by colorization of a grey-level image hiding its color palette. ACM Trans Graph 31 6 — Google Scholar. Devi MS, Mandowara A Extended performance comparison of pixel window size for colorization of grayscale images using yuv color space.

Du W Colorization using the information of prototypes and edges. Kawulok M, Smolka B Competitive image colorization. Kumar S, Swarnkar A Gray image colorization in ycbcr color space. Trans Image Process 22 7 — In: Transactions on Graphics, Vol. Pattern recognition letters 28 12 — Pellacini F, Lawrence J Appwand: editing measured materials using appearance-driven optimization. ACM Trans Graph 26 3 Journal on Imaging Sciences 8 1 — Rusu C, Tsaftaris S et al Estimation of scribble placement for painting colorization.

In: International conference on consumer electronics. Computer Graphics and Applications 2 — In: Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering, ACM, pp — Wang S, Zhang Z Colorization by matrix completion.This paper presents a novel colorization technique for hand-drawn grayscale images, such as cartoons and sketches, based on reference natural images and simple user interactions, which can generate colorful and natural results.

Firstly, to solve the problem of inputting complex color scribbles by simple interactions, we introduce a region thinning method for generating color scribbles and then map these scribbles to the corresponding regions in the hand-drawn image via region mapping method based on the scanning line.

Secondly, to maintain the color fidelity, a luminance downward modification method is proposed. Next, a optimization method for colorization is proposed, where the feature vectors are adjusted by a smooth feature map, thus maintaining smooth color transitions in the smooth areas and controlling the color overflow in the texture areas.

Thirdly, to fuse the colorized image with a highlight effect, a luminance upward modification method by weights, which are determined by color distance and boundary distance, is proposed. The experimental results of the proposed algorithm show the smooth color transition in the intensity-continuity areas, color overflow controlling in the texture-continuity areas and natural effect of light and shadow.

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Welsh, T. Acm Trans. Liu, S. Pattern Recognit. Gauge, C. Google Scholar. Imaging 22 1 Arbelot, B. Chen, T. Citeseer Irony, R. Schnitman, Y. In: Asian Conference on Computer Vision, pp. Li, B. IEEE Trans. Image Process. Charpiat, G. Iizuka, S. ACM Trans. Zhang, R. Springer, Berlin Luo, X. Furusawa, C.

Hand-drawn grayscale image colorful colorization based on natural image

ACM Levin, A. Kawulok, M. Koo, H. Imaging 20 1— Yatziv, L. Qu, Y.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Estimation of scribble placement for painting colorization Abstract: Image colorization has been a topic of interest since the mid 70's and several algorithms have been proposed that given a grayscale image and color scribbles hints produce a colorized image.

However, the questions of what is the minimum number of scribbles necessary and where they should be placed in an image remain unexplored.

Estimation of scribble placement for painting colorization

Here we address this limitation using an iterative algorithm that provides insights as to the relationship between locally vs. Given a color image we randomly select scribbles and we attempt to color the grayscale version of the original.

We define a scribble contribution measure based on the reconstruction error. We demonstrate our approach using a widely used colorization algorithm and images from a Picasso painting and the peppers test image. We show that areas isolated by thick brushstrokes or areas with high textural variation are locally important but contribute very little to the overall representation accuracy. The proposed method can be used verbatim to test any colorization algorithm.

Article :. DOI: Need Help?Rusu, Cristian and Tsaftaris, Sotirios A.

Efficient image colorization based on seed pixel selection

IEEE, Trieste, pp. ISBN Image colorization has been a topic of interest since the mid 70's and several algorithms have been proposed that given a grayscale image and color scribbles hints produce a colorized image.

However, the questions of what is the minimum number of scribbles necessary and where they should be placed in an image remain unexplored. Here we address this limitation using an iterative algorithm that provides insights as to the relationship between locally vs.

Given a color image we randomly select scribbles and we attempt to color the grayscale version of the original. We define a scribble contribution measure based on the reconstruction error.

We demonstrate our approach using a widely used colorization algorithm and images from a Picasso painting and the peppers test image. We show that areas isolated by thick brushstrokes or areas with high textural variation are locally important but contribute very little to the overall representation accuracy.

The proposed method can be used verbatim to test any colorization algorithm. Estimation of scribble placement for painting colorization. ISBN Full text not available from this repository. Staff Login. Dublin 1. Computer science.Grenoble v Chambly Thelle FC Slavia Prague v FC Astana Villarreal v Maccabi Tel Aviv Young Boys v Skenderbeu Dynamo Kiev v Partizan Belgrade Istanbul Basaksehir v Braga TSG Hoffenheim v Ludogorets Razgrad HNK Rijeka v AC Milan FK Austria Vienna v AEK Athens Apollon Limassol v Everton.

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Procreate: How to Colorize Grayscale Painting

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