Appearance Harmonization for Single Image Shadow Removal

Ma, Liqian; Wang, Jue; Shechtman, Eli; Sunkavalli, Kalyan; Hu, Shi‐Min

doi:10.1111/cgf.13016

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Ma et al [19] used a patch-based image synthesis approach that reconstructs the shadow region using patches sampled from non-shadow regions. The color and texture of shadow patches were then modified based on correction parameters.…”

Section: Patch-based Methodsmentioning

confidence: 99%

A Survey on Shadow Removal Techniques for Single Image

Murali¹,

Govindan²,

Kalady³

2016

IJIGSP

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Shadows are physical phenomena that appear on a surface when direct light from a source is unable to reach the surface due to the presence of an object between the source and the surface. The formation of shadows and their various features has evolved as a topic of discussion among researchers. Though the presence of shadows can aid us in understanding the scene model, it might impair the performance of applications such as object detection. Hence, the removal of shadows from videos and images is required for the faultless working of certain image processing tasks. This paper presents a survey of notable shadow removal techniques for single image available in the literature. For the purpose of the survey, the various shadow removal algorithms are classified under five categories, namely, reintegration methods, relighting methods, patch-based methods, color transfer methods, and interactive methods. Comparative study of qualitative and quantitative performances of these works is also included. The pros and cons of various approaches are highlighted. The survey concludes with the following observations-(i) shadow removal should be performed in real time since it is usually considered as a preprocessing task, (ii) the texture and color information of the regions underlying the shadow must be recovered, (iii) there should be no hard transition between shadow and nonshadow regions after removing the shadows.

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Section: Patch-based Methodsmentioning

confidence: 99%

A Survey on Shadow Removal Techniques for Single Image

Murali¹,

Govindan²,

Kalady³

2016

IJIGSP

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“…Formulating shadow enhancement as local tone adjustment and using edge-preserving histogram manipulation [Kaufman et al 2012] enables contrast enhancement on semantically segmented photographs. Relative differences in the material and illumination of paired image segments [Guo et al 2012;Ma et al 2016] enables the training of region-based classifiers and the use of graph cuts for labeling and shadow removal. Shadow removal has also been formulated as an entropy minimization problem [Finlayson et al 2009[Finlayson et al , 2002, where invariant chromaticity and intensity images are used to produce a shadow mask that is then re-integrated to form a shadow-free image.…”

Section: Related Workmentioning

confidence: 99%

“…These methods assume that shadow regions contain approximately constant reflectance and that image gradients are entirely due to changes in illumination, and are thereby fail when presented with complex spatially-varying textures or soft shadowing. In addition, by decomposing the shadow removal problem into two separate stages of detection and manipulation, these methods cannot recover from errors during the shadow detection step [Ma et al 2016].…”

Section: Related Workmentioning

confidence: 99%

Portrait shadow manipulation

et al. 2020

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Fig. 1. The results of our portrait enhancement method on real-world portrait photographs. Casual portrait photographs often suffer from undesirable shadows, particularly foreign shadows cast by external objects, and dark facial shadows cast by the face upon itself under harsh illumination. We propose an automated technique for enhancing these poorly-lit portrait photographs by removing unwanted foreign shadows, reducing harsh facial shadows, and adding synthetic fill lights. Casually-taken portrait photographs often suffer from unflattering lighting and shadowing because of suboptimal conditions in the environment. Aesthetic qualities such as the position and softness of shadows and the lighting ratio between the bright and dark parts of the face are frequently determined by the constraints of the environment rather than by the photographer. Professionals address this issue by adding light shaping tools such as scrims, bounce cards, and flashes. In this paper, we present a computational approach that gives casual photographers some of this control, thereby

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“…In early work, color [15,20], edge [18], or segmentation [19] cues was used to build high level features for shadow description. Ma et al [21] introduced appearance harmonization that makes the appearance of a deshadowed region compatible with the rest of the image. Recently, convolutional neural networks for shadow removal have been proposed [16,17].…”

Section: Shadow Detection and Removalmentioning

confidence: 99%

ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks

2019

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We introduce ShadowGAN, a generative adversarial network (GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows, and an input source object with a specified insertion position, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow's appearance is locally realistic in shape, and globally consistent with other objects' shadows in terms of shadow direction and area. To overcome the lack of training data, we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic.

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Appearance Harmonization for Single Image Shadow Removal

Cited by 10 publications

References 26 publications

A Survey on Shadow Removal Techniques for Single Image

A Survey on Shadow Removal Techniques for Single Image

Portrait shadow manipulation

ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks

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