Image colorization using similar images

Gupta, Raj Kumar; Chia, Alex Yong-Sang; Rajan, Dinesh; Ng, Ee Sin; Huang, Zhiyong

doi:10.1145/2393347.2393402

Cited by 235 publications

(191 citation statements)

References 21 publications

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“…For instance, the technique of Gupta et al [6] uses the chrominance diffusion approach of Levin et al [1].…”

Section: Variational Methods For Chrominance Postprocessingmentioning

confidence: 99%

“…In this section, we compare our method with -the patch-based algorithm of Welsh et al [8], -the patch-based method of Pierre et al [3], and -the segmentation approach of Gupta et al [6].…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Recovering an RGB image from the luminance channel alone is an ill-posed problem and requires additional information [3,6]. This information is provided in the literature in two different ways, namely by manual [1,4,7] or exemplar-based [3,6,8] methods. In the first one the user augments the image by some color strokes as basis for the algorithm to compute the color of each pixel.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Gupta et al [6] use SURF and Gabor features, Irony et al [2] use DCT, Welsh et al [8] use standard-deviation, etc. Some of them are done after an automatic segmentation, whereas others use local information.…”

Section: Introductionmentioning

confidence: 99%

“…The matching between segments of the images is computed from DCT descriptors which analyse the textures. The method of Gupta et al [6] is rather similar. Here, an over-segmentation (SLIC, see, e.g., [10]) is used instead of the mean-shift algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Exemplar-Based Face Colorization Using Image Morphing

2017

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Colorization of gray-scale images relies on prior color information. Exemplar-based methods use a color image as source of such information. Then the colors of the source image are transferred to the gray-scale target image. In the literature, this transfer is mainly guided by texture descriptors. Face images usually contain few texture so that the common approaches frequently fail. In this paper, we propose a new method taking the geometric structure of the images rather their texture into account such that it is more reliable for faces. Our approach is based on image morphing and relies on the YUV color space. First, a correspondence mapping between the luminance Y channel of the color source image and the gray-scale target image is computed. This mapping is based on the time discrete metamorphosis model suggested by Berkels, Effland and Rumpf. We provide a new finite difference approach for the numerical computation of the mapping. Then, the chrominance U,V channels of the source image are transferred via this correspondence map to the target image. A possible postprocessing step by a variational model is developed to further improve the results. To keep the contrast special attention is paid to make the postprocessing unbiased. Our numerical experiments show that our morphing based approach clearly outperforms state-of-the-art methods.

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“…For instance, the technique of Gupta et al [6] uses the chrominance diffusion approach of Levin et al [1].…”

Section: Variational Methods For Chrominance Postprocessingmentioning

confidence: 99%

“…In this section, we compare our method with -the patch-based algorithm of Welsh et al [8], -the patch-based method of Pierre et al [3], and -the segmentation approach of Gupta et al [6].…”

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exemplar-Based Face Colorization Using Image Morphing

2017

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Real‐time coloring method of laser surgery video based on generative adversarial network

Pan¹,

Lian

et al. 2023

Int J Imaging Syst Tech

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Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real-time colorization of the laser surgery video under black-andwhite imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real-time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736.

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Semantic‐aware automatic image colorization via unpaired cycle‐consistent self‐supervised network

Xiao

Jiang

Liu

et al. 2021

Int J of Intelligent Sys

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Automatic image colorization without manual interventions is an ill-conditioned and inherently ambiguous problem. Most of existing methods focus on formulating colorization as a regression problem and learn parametric mappings from grayscale to color through deep neural networks. Due to the multimodalities of color-grayscale space, in many applications, it is not required to recover exact ground-truth color. Pair-wise pixel-to-pixel learning-based algorithms lack rationality. Techniques such as color space conversion techniques are then proposed to avoid such direct pixel learning. However, the coloring results after color space conversion are blunt and unnatural. In this paper, we hold viewpoints that a reasonable solution is to generate some colorized result that looks natural. No matter what color a region is to be assigned, the colorized region should be semantically and spatially consistent. In this paper, we propose an effective semantic-aware automatic colorization model via unpaired cycle-consistent self-supervised network. Low-level monochrome loss, perceptual identity loss and high-level semantic-consistence loss, together with adversarial loss, are introduced to guide network selftraining. We train and test our model on randomly selected subsets from PASCAL VOC 2012. The

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Image colorization using similar images

Cited by 235 publications

References 21 publications

Exemplar-Based Face Colorization Using Image Morphing

Exemplar-Based Face Colorization Using Image Morphing

Real‐time coloring method of laser surgery video based on generative adversarial network

Semantic‐aware automatic image colorization via unpaired cycle‐consistent self‐supervised network

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