Automatic shadow detection and removal using image matting

Amin, Benish; Riaz, Muhammad Mohsin; Ghafoor, Abdul

doi:10.1016/j.sigpro.2019.107415

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…For example, Zhang proposed a robust vehicle detection method with shadow elimination [ 11 ]. Amin Benish proposed a shadow mask extractor by using a three color attenuation model (TAM) and intensity information to segment the shadow area [ 12 ]. Saritha Murali proposed a method to remove shadows from images with uniform textures models [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

A differential correction based shadow removal method for real-time monitoring

Liu

Chen

et al. 2023

PLoS ONE

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Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.

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Section: Related Workmentioning

confidence: 99%

A differential correction based shadow removal method for real-time monitoring

Liu

Chen

et al. 2023

PLoS ONE

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“…. Remove umbra: UI umbra (x, y) = I(x, y) × η(x, y) 4: Penumbra removal using LBWF-based algorithm. Obtain a binarization image B 1 from UI umbra using integral image technique, which corresponds to Figure 4b.…”

Section: Local Binarized Water-fillingmentioning

confidence: 99%

“…Optical shadows appear out of nowhere in the images captured from camera sensors [1][2][3][4]. They are generated when light sources are occluded by static or moving objects [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Wang

Chen

2020

Sensors

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Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

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“…With the advantages of high resolution, low acquisition cost, and fast acquisition speed, unmanned aerial vehicle (UAV) images have become popular in practice for collecting data and mapping [3]. However, due to the compound effect of solar illumination, ground reflection, and atmospheric disturbance, UAV remote sensing images widely suffer from the problem of low recognition of color features and texture features in shadow regions, which seriously reduces the quality of UAV remote sensing images, and then has serious impact on subsequent image processing tasks such as image interpretation, image matching, feature extraction, land cover classification, and digital photogrammetry [4][5][6][7]. Although there are ways to automatically balance in the case of UAV cameras, for example, the DJI GO app has automatic light balance in order to avoid darkness or high exposure, it can only optimize for uneven lighting, not eliminate shadows.…”

Section: Introductionmentioning

confidence: 99%

Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions

et al. 2022

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Due to imaging and lighting directions, shadows are inevitably formed in unmanned aerial vehicle (UAV) images. This causes shadowed regions with missed and occluded information, such as color and texture details. Shadow detection and compensation from remote sensing images is essential for recovering the missed information contained in these images. Current methods are mainly aimed at processing shadows with simple scenes. For UAV remote sensing images with a complex background and multiple shadows, problems inevitably occur, such as color distortion or texture information loss in the shadow compensation result. In this paper, we propose a novel shadow removal algorithm from UAV remote sensing images based on color and texture equalization compensation of local homogeneous regions. Firstly, the UAV imagery is split into blocks by selecting the size of the sliding window. The shadow was enhanced by a new shadow detection index (SDI) and threshold segmentation was applied to obtain the shadow mask. Then, the homogeneous regions are extracted with LiDAR intensity and elevation information. Finally, the information of the non-shadow objects of the homogeneous regions is used to restore the missed information in the shadow objects of the regions. The results revealed that the average overall accuracy of shadow detection is 98.23% and the average F1 score is 95.84%. The average color difference is 1.891, the average shadow standard deviation index is 15.419, and the average gradient similarity is 0.726. The results have shown that the proposed method performs well in both subjective and objective evaluations.

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Automatic shadow detection and removal using image matting

Cited by 13 publications

References 24 publications

A differential correction based shadow removal method for real-time monitoring

A differential correction based shadow removal method for real-time monitoring

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions

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