Interactive local adjustment of tonal values

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

doi:10.1145/1141911.1141936

Cited by 337 publications

(258 citation statements)

References 17 publications

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“…The guarded regions are propagated pixel values flexibly all over the entire image according to the guided information. By using edge preserving energy minimization method generate region aware mask generated which was originally proposed by Lischiniski et al [4] for tonal adjustment.…”

Section: Adaptive Region Aware Maskmentioning

confidence: 99%

“…The second term in equation (1) is the smoothing term, which is responsible for keeping the gradients of the mask M as low as possible, the main factor represent the G called as gradient. We generalize the guided image from the original model of Lischiniski et al [4] to a guided feature space, which means that richer facial information can be used to guide the propagation. Three parameters control propagation in the smoothing term: ε is a small constant that prevents division by zero, λ is used to balance the relative weights of the two terms, and α controls the propagation sensitivity to the gradients of G.…”

Section: Adaptive Region Aware Maskmentioning

confidence: 99%

“…The facial skin appearance and color are important attractive factors, and mostly people consider that a good picture should be blemish free and have smooth facial colors.The proposed method of an automatic region aware maskovercome the limitations of these method for facial skin smoothening, by using edge preserving energy minimization framework introduced by Lischiniski et al [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Facial Skin Smoothening by Iterative Steering Kernel Regression Technique

Mehra¹,

Deshpande²

2017

International Journal of Innovative Research in Electrical, Ele

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The paper enhanced the facial skin using iterative steering kernel regression which is based on edge preserving energy minimization. The region-aware mask allows the user to beautify faces with three major skins attributes homogeneity, lighting, and color. The main aim of this paper is to beautify the skin by decomposition into the three layers using edge preserving filter. After layer decomposition a facial landmarks and significant features extracted from the database to generate the mask into three different layers which allow the user to perform facial beautification simply by adjusting its skin parameter. The combination of these parameters can be optimized automatically it depend on the data base or knowledge base. The experiment result shows that the image is enhanced more as compare to the original image. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Result of proposed method will be compared with the existing methods in terms of Peak Signal to noise ratio (PSNR) and Mean Square Error (MSE).

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Section: Adaptive Region Aware Maskmentioning

confidence: 99%

Section: Adaptive Region Aware Maskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Facial Skin Smoothening by Iterative Steering Kernel Regression Technique

Mehra¹,

Deshpande²

2017

International Journal of Innovative Research in Electrical, Ele

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“…Liu et al [21] decompose images into illumination and reflectance layers, and transfer color to grayscale reflectance images using a similar color propagation scheme. Lischinski et al [22] extend the framework for image tone manipulation, propagating user constraints with edge-preserving optimization. A similar method is used in Ref.…”

Section: Edit Propagationmentioning

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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“…Methods can be semi-automatic [20] or automatic. Automatic methods usually work well under quite strong assumptions, like hard shadows and black-body radiators lights [19] or localized gray-world model [8].…”

Section: Related Workmentioning

confidence: 99%

Flash Lighting Space Sampling

Dellepiane

Callieri

Corsini

et al. 2009

Computer Vision/Computer Graphics CollaborationTechniques

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Abstract. Flash light of digital cameras is a very useful way to picture scenes with low quality illumination. Nevertheless, especially low-end cameras integrated flash lights are considered as not reliable for high quality images, due to known artifacts (sharp shadows, highlights, uneven lighting) generated in images. Moreover, a mathematical model of this kind of light seems difficult to create. In this paper we present a color correction space which, given some information about the geometry of the pictured scene, is able to provide a space-dependent correction of each pixel of the image. The correction space can be calculated once in a lifetime using a quite fast acquisition procedure; after 3D spatial calibration, obtained color correction function can be applied to every image where flash is the dominant illuminant. The correction space presents several advantages: it is independent from the kind of light used (provided that it is bound to the camera), it gives the possibly to correct only determinate artifacts (for example color deviation) introduced by flash light, and it has a wide range of possible applications, from image enhancement to material color estimation.

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Interactive local adjustment of tonal values

Cited by 337 publications

References 17 publications

Facial Skin Smoothening by Iterative Steering Kernel Regression Technique

Facial Skin Smoothening by Iterative Steering Kernel Regression Technique

Lighting transfer across multiple views through local color transforms

Flash Lighting Space Sampling

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