Interactive local adjustment of tonal values

Lischinski, Dani; Farbman, Zeev; Uyttendaele, Matt; Szeliski, Richard

doi:10.1145/1179352.1141936

Cited by 84 publications

(135 citation statements)

References 14 publications

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“…Edit parameters are propagated from stroked pixels to non-stroked pixels on the assumption that similarly colored pixels close to each other should receive similar edit parameters. Edit propagation has been applied to many image-editing applications, including grayscale image colorization [18], color-to-gray image conversion [33], high-dynamic-range image manipulation [20], material manipulation [25,36] and color transfer [3]. Edit propagation is usually formulated by quadratic energy minimization.…”

Section: Related Workmentioning

confidence: 99%

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Sparse pixel sampling for appearance edit propagation

Yatagawa

Yamaguchi

2015

Vis Comput

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Edit propagation is an appearance-editing method using sparsely provided edit strokes from users. Although edit propagation has a wide variety of applications, it is computationally complex, owing to the need to solve large linear systems. To reduce the computational cost, interpolation-based approaches have been studied intensely. This study is inspired by an interpolation-based edit-propagation method that uses a clustering algorithm to determine samples. The method uses an interpolant, which approximates edit parameters with convex combinations of the samples. However, because the clustering algorithm generates samples that lie inside the set of pixels in a feature space, an interpolant with convex combinations does not allow for an exact reconstruction of the pixels outside the convex hull. To address this issue, this paper proposes a novel approximation model for interpolating image colors as well as edit parameters using affine combinations. In addition, this paper introduces sparse pixel sampling to determine the quantity and positions of samples and the weight coefficients of the affine combinations simultaneously. Sparse pixel sampling is performed by updating candidate pixels. Unnecessary pixels are discarded with compressive sensing, and new candidate pixels are greedily resampled following their approximation errors. This paper demonstrates that the B Tatsuya Yatagawa proposed model achieves better approximation in terms of both image colors and edit parameters, and discusses the properties of the proposed model with various experiments.

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Section: Related Workmentioning

confidence: 99%

“…The edit parameters might represent, for example, the difference in color between the original image and the edited result. Edit propagation has been applied to various image-editing applications, such as image colorization [18], high-dynamic-range image manipulation [20], and material and color manipulation [2,25].…”

Section: Introductionmentioning

confidence: 99%

Sparse pixel sampling for appearance edit propagation

Yatagawa

Yamaguchi

2015

Vis Comput

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“…Consequently, based on the Markov random field theory, we put forward an energy optimization algorithm which can effectively correct the tonal distribution of the image. Although a number of energy formulations have been proposed before [4,17,18], we consider the following optimization framework to perform the tonal correction. Given an input image g and an enhanced image u after above detail enhancement process, we seek a new image f through the energy optimization method, which is as close as possible to the tonal distribution of g and as far as possible to approximate the gradient distribution of u (see Fig.…”

Section: Energy Optimization Based Tonal Correction Algorithmmentioning

confidence: 99%

Adaptive tone-preserved image detail enhancement

et al. 2012

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An adaptive tone-preserved algorithm for image detail enhancement is proposed to retain the tonal distribution of the input image and avoid experiential manipulation. First of all, domain transform based multi-scale image decomposition is carried out to quickly divide the input image into a base image which contains the coarse-scale image information, and the detail layers which contain the finescale details. Then, during the process of detail enhancement and synthesis, we construct an adaptive detail enhancement function based on the edge response, to prevent the exaggeration of strong edges and increase the enhancing magnitude of small details. Finally, in order to keep the color values of the input image and the gradient values of the detail enhanced image, a tonal correction algorithm based on energy optimization is presented to eliminate the distinct tonal differences of the enhanced image from the input image. Our experimental results show that tone-consistent image detail enhancement effect is available for arbitrary input images with unified parameters setting, which is superior to the state-of-the-art methods.

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“…In general, first the user marks the region of interest with a few strokes, and specifies constraints on the marked regions, then propagates the constraints to the entire image by solving an optimization problem. These methods have been proved effective for images [13][14] as well as materials [15][16] . However, they are generally limited to editing a single image or a kind of material, not addressing the editing problem in pasting a source image onto a target image.…”

Section: Appearance Editingmentioning

confidence: 99%

Free Appearance-Editing with Improved Poisson Image Cloning

Bie

Huang

Wang

2011

J. Comput. Sci. Technol.

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In this paper, we present a new edit tool for the user to conveniently preserve or freely edit the object appearance during seamless image composition. We observe that though Poisson image editing is effective for seamless image composition. Its color bleeding (the color of the target image is propagated into the source image) is not always desired in applications, and it provides no way to allow the user to edit the appearance of the source image. To make it more flexible and practical, we introduce new energy terms to control the appearance change, and integrate them into the Poisson image editing framework. The new energy function could still be realized using efficient sparse linear solvers, and the user can interactively refine the constraints. With the new tool, the user can enjoy not only seamless image composition, but also the flexibility to preserve or manipulate the appearance of the source image at the same time. This provides more potential for creating new images. Experimental results demonstrate the effectiveness of our new edit tool, with similar time cost to the original Poisson image editing.

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Interactive local adjustment of tonal values

Cited by 84 publications

References 14 publications

Sparse pixel sampling for appearance edit propagation

Sparse pixel sampling for appearance edit propagation

Adaptive tone-preserved image detail enhancement

Free Appearance-Editing with Improved Poisson Image Cloning

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