Progressive color transfer for images of arbitrary dynamic range

Pouli, Tania; Reinhard, Erik

doi:10.1016/j.cag.2010.11.003

Cited by 106 publications

(92 citation statements)

References 19 publications

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“…In image inpainting, for example, knowing what important statistics the missing texture should have is critical [18]. In transferring image characteristics, better understanding of image statistics and the correlation would help design better algorithms to capture and transfer those aesthetic properties [24]. Despite cultural prominence as canonical "stimuli", scientists only recently began to conduct more rigorous investigations into the statistics of imagery properties of artworks [9].…”

Section: Applications and Challengesmentioning

confidence: 99%

Visualizing Natural Image Statistics

Fang

Tam

Borgo

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics.

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Section: Applications and Challengesmentioning

confidence: 99%

Visualizing Natural Image Statistics

Fang

Tam

Borgo

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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“…Work in [4] proposed gradient-preserving model that consider both color distribution and the scene details. An extended Principal Component Analysis (PCA) based transfer is used to manage the color range, and a minimization scheme is the base to generate color features.…”

Section: Prior Workmentioning

confidence: 99%

“…The use of statistical analysis can greatly simplify the computational load of learning colors, provided it can be successfully matched and optimized for the problem at hand. Recent works by [2], [3], [4], [5] have made great strides in scaling both automatic and custom-handled color transfer, though context consistency of these models remains unremarkable.…”

Section: Introductionmentioning

confidence: 99%

Smooth Context based Color Transfer

Anh¹

2015

IJCA

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Color transfer is an emerging framework for dealing with ubiquitous color manipulation in media such as documents and images. Despite the notable progress made in the field, there remains a need for designers that can represent the same information in personalization and corresponding to media context. This work presents adaptive color transfer method using cross-disciplinary interaction of semantic context and bilateral filters. Colors in the method are transferred softly in matching with saliency distributed context. Preliminary results show that the framework is highly keeping consistency and promising. Consequently in this work, a solution of tone mapping by color transfer is introduced. Experimental results are further showed pertaining for automatic handling colors and contrast.

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“…Example-based transfer methods such as those in Refs. [1][2][3] reshape the color distribution of the input image so that it approaches the statistical color properties of the example image. Huang et al [4] recolor a photo by learning from database correlations between color property distributions and geometric features of regions.…”

Section: Color Transfer and Correctionmentioning

confidence: 99%

Lighting transfer across multiple views through local color transforms

2017

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We present a method for transferring lighting between photographs of a static scene. Our method takes as input a photo collection depicting a scene with varying viewpoints and lighting conditions. We cast lighting transfer as an edit propagation problem, where the transfer of local illumination across images is guided by sparse correspondences obtained through multi-view stereo. Instead of directly propagating color, we learn local color transforms from corresponding patches in pairs of images and propagate these transforms in an edge-aware manner to regions with no correspondences. Our color transforms model the large variability of appearance changes in local regions of the scene, and are robust to missing or inaccurate correspondences. The method is fully automatic and can transfer strong shadows between images. We show applications of our image relighting method for enhancing photographs, browsing photo collections with harmonized lighting, and generating synthetic time-lapse sequences.

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Progressive color transfer for images of arbitrary dynamic range

Cited by 106 publications

References 19 publications

Visualizing Natural Image Statistics

Visualizing Natural Image Statistics

Smooth Context based Color Transfer

Lighting transfer across multiple views through local color transforms

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