A Survey on Shadow Removal Techniques for Single Image

Murali, S.; Govindan, V. K.; Kalady, Saidalavi

doi:10.5815/ijigsp.2016.12.05

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Our review of the related research focuses primarily on methods involving deep learning-based shadow removal because the objective of this study is to investigate these methods. Comprehensive surveys on shadow detection and removal methods can be found in previously published literature [21][22][23][24]. Hu et al [6] proposed the Mask-ShadowGAN for learning to remove shadows from unpaired training data by extending CycleGAN [25]; they modified CycleGAN to learn the underlying relationships between the shadow and shadow-free domains with the guidance of shadow masks, which are also learned from shadow images automatically.…”

Section: Related Workmentioning

confidence: 99%

C2ShadowGAN: cycle-in-cycle generative adversarial network for shadow removal using unpaired data

et al. 2022

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Recent advances in deep learning technology, and the availability of public shadow image datasets, have enabled significant performance improvements of shadow removal tasks in computer vision. However, most deep learning-based shadow removal methods are usually trained in a supervised manner, in which paired shadow and shadow-free data are required. We developed a weakly supervised generative adversarial network with a cycle-in-cycle structure for shadow removal using unpaired data. In addition, we introduced new loss functions to reduce unnecessary transformations for non-shadow areas and to enable smooth transformations for shadow boundary areas. We conducted extensive experiments using the ISTD and Video Shadow Removal datasets to assess the effectiveness of our methods. The experimental results show that our method is superior to other state-of-the-art methods trained on unpaired data.

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Section: Related Workmentioning

confidence: 99%

C2ShadowGAN: cycle-in-cycle generative adversarial network for shadow removal using unpaired data

et al. 2022

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“…There exists a multitude of shadow detection (and removal) algorithms [10]. They leverage pixel intensity properties [11,12,13,14,15,16] and texture features [15,17,18,19].…”

Section: Related Workmentioning

confidence: 99%

Drone Shadow Tracking

Zou,

Zhou,

Helou

et al. 2019

Preprint

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Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the shadow of the drone must be tracked.Tracking a drone's shadow in a video is, however, challenging. The varying size, shape, change of orientation and drone altitude pose difficulties. The shadow can also easily disappear over dark areas. However, a shadow has specific properties that can be leveraged, besides its geometric shape. In this paper, we incorporate knowledge of the shadow's physical properties, in the form of shadow detection masks, into a correlation-based tracking algorithm. We capture a test set of aerial videos taken with different settings and compare our results to those of a state-of-the-art tracking algorithm.

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“…Removal of shadows is often considered to be a relighting task in which the brightness of shadow pixels is increased to make them as well illuminated as the non-shadow surroundings. In the survey [5], shadow removal techniques are categorized into reintegration methods [6], relighting methods [7], patch-based methods [8], and color transfer methods [9]. Furthermore, these techniques are either automatic [7] or interactive [10], depending on whether the user is provided an interface to incorporate his knowledge into the system.…”

Section: Introductionmentioning

confidence: 99%

Single image shadow removal by optimization using non-shadow anchor values

2019

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Shadow removal has evolved as a preprocessing step for various computer vision tasks. Several studies have been carried out over the past two decades to eliminate shadows from videos and images. Accurate shadow detection is an open problem because it is often considered difficult to interpret whether the darkness of a surface is contributed by a shadow incident on it or not. This paper introduces a color-model based technique to remove shadows from images. We formulate shadow removal as an optimization problem that minimizes the dissimilarities between a shadow area and its non-shadow counterpart. To achieve this, we map each shadow region to a set of non-shadow pixels, and compute an anchor value from the non-shadow pixels. The shadow region is then modified using a factor computed from the anchor value using particle swarm optimization. We demonstrate the efficiency of our technique on indoor shadows, outdoor shadows, soft shadows, and document shadows, both qualitatively and quantitatively.

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A Survey on Shadow Removal Techniques for Single Image

Cited by 8 publications

References 29 publications

C2ShadowGAN: cycle-in-cycle generative adversarial network for shadow removal using unpaired data

C2ShadowGAN: cycle-in-cycle generative adversarial network for shadow removal using unpaired data

Drone Shadow Tracking

Single image shadow removal by optimization using non-shadow anchor values

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