Auto-Exposure Fusion for Single-Image Shadow Removal

Fu, Lei; Zhou, Changqing; Guo, Qing; Juefei-Xu, Felix; Yu, Hongkai; Feng, Wei; Liu, Yang; Wang, Song

doi:10.1109/cvpr46437.2021.01043

Cited by 110 publications

(129 citation statements)

References 28 publications

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“…SRD: We report and compare our shadow removal results in both -constrained and unconstrained setup with existing fullysupervised methods on SRD [28] using RMSE metric. Table VII indicates that our proposal trained in fully-supervised fashion obtains the lowest RMSE in all regions and outperforms the most recent state-of-the-art methods [37], [48].…”

Section: Quantitative Studymentioning

confidence: 88%

“…Weakly-supervised method G2R-ShadowNet [36] designed three sub-networks dedicated for shadow generation, shadow removal and image refinement. Fu et al [37] modelled the shadow removal problem from a different perspective that is auto-exposure fusion. They proposed shadow-aware FusionNet and boundary-aware Re-fineNet to obtain final shadow removed image.…”

Section: B Deep Learning Based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal

Dasgupta¹,

Das²,

Das³

et al. 2022

Preprint

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Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It comprises of a DeShadower network responsible for removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artifacts. We show that UnShadowNet can also be easily extended to a fully-supervised setup to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups.

show abstract

Section: Quantitative Studymentioning

confidence: 88%

Section: B Deep Learning Based Approachesmentioning

confidence: 99%

UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal

Dasgupta¹,

Das²,

Das³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It's limited to recognizable reflections and fails in extremely strong lights where image content knowledge is not available. The same principle applies to shadow conditions as well, where the original image element is intact with a little low brightness in certain regions [195]. Such image processing uses similar computer vision techniques as in previous paragraphs and can also take the road of first generating shadows then removing them [202].…”

Section: Light Relatedmentioning

confidence: 96%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

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“…Predictive filtering based image restoration. The predictive filtering has been widely used in image restoration tasks, e.g., denoising [1,21], deraining [13], shadow removing [9], and blur synthesis [4]. The predictive filtering allows more focused learning of the surrounding information for each pixel.…”

Section: Related Workmentioning

confidence: 99%

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

Li¹,

Guo²,

Dong³

et al. 2022

Preprint

Self Cite

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Auto-Exposure Fusion for Single-Image Shadow Removal

Cited by 110 publications

References 28 publications

UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal

UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

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