Color transfer between images

Reinhard, Erik; Adhikhmin, M.; Gooch, Bruce; Shirley, Peter

doi:10.1109/38.946629

Cited by 2,475 publications

(1,909 citation statements)

References 7 publications

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“…These approaches include color transfer [4,21,23,24] which is a color correction method for borrowing one images color characteristics from another, photomosaic [13][14][15]22] which represents a target image by assembling given photographic examples, collage [12]. In this study, we concentrate mainly on the style transfer approach.…”

Section: Related Workmentioning

confidence: 99%

Perceptually inspired real-time artistic style transfer for video stream

Kang

Tian

Seo

2016

J Real-Time Image Proc

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This study presents a real-time texture transfer method for artistic style transfer for video stream. We propose a parallel framework using a T-shaped kernel to enhance the computational performance. With regard to accelerated motion estimation, which is necessarily required for maintaining temporal coherence, we present a method using a downscaled motion field to successfully achieve high real-time performance for texture transfer of video stream. In addition, to enhance the artistic quality, we calculate the level of abstraction using visual saliency and integrate it with the texture transfer algorithm. Thus, our algorithm can stylize video with perceptual enhancements.

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Section: Related Workmentioning

confidence: 99%

Perceptually inspired real-time artistic style transfer for video stream

Kang

Tian

Seo

2016

J Real-Time Image Proc

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“…The first color transfer algorithm [10] was proposed by Reinhard et al and it was later improved [7] by Welsh et al The latter method matches simple textural and luminance information and can be performed automatically, but gives better results with user assistance. First, a set of 200 pixels is randomly selected from the reference image.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Textural Features for Image and Video Colorization

Kawulok

Smołka

2012

IEICE Trans. Inf. & Syst.

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SUMMARYImage 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.

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“…Albeit we do not exactly affirm the colors of the photo, we still could map the colors of this scene of today to the gray scale image using color transferring technologies based on texture synthesis. [1] [3] Video analogy was derived from image analogy [19][4]. Assume we have two similar videos at hand, we therefore create a relationship and bridge the gap between two videos.…”

Section: Related Workmentioning

confidence: 99%

Event Analogy Based Privacy Preservation in Visual Surveillance

Yan

Liu

2016

Image and Video Technology – PSIVT 2015 Workshops

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Abstract. Privacy preservation as a thorny problem in surveillance has been arisen because of its relevance to human right, however it has not been completely solved yet today. In this paper, we investigate this existing problem and expect to get ride of those intuitive methods such as pixelization, blurring or mosaicking on human face regions through object tracking. We detail privacy preservation at event level and thereafter choose suitable events represented by motion pictures in virtual reality to replace those events of surveillance in real reality. The advantage of taking use of this approach is to leverage the utility and privacy of surveillance events. The outcome will not affect visual effects and surveillance security much however it is able to achieve the objectives of privacy preservation.

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Color transfer between images

Cited by 2,475 publications

References 7 publications

Perceptually inspired real-time artistic style transfer for video stream

Perceptually inspired real-time artistic style transfer for video stream

Discriminative Textural Features for Image and Video Colorization

Event Analogy Based Privacy Preservation in Visual Surveillance

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