A New Shadow Removal Method Using Color-Lines

Yu, Xiaoming; Li, Ge; Ying, Zhenqiang; Guo, Xiaoqiang

doi:10.1007/978-3-319-64698-5_26

Cited by 18 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be concluded that the model proposed by Yang [28] had the worst overall performance, since it was easy to change the non-shaded area in the original image during the shadow removal process, which resulted in poor similarity between the two. The image shadow removal model proposed by Yu [21] and Gong [15] was relatively better than other algorithms, but it was inferior to the RSnet model proposed in this paper. The RSnet model had more stability, and the image shadow processing effect was better.…”

Section: Performance Comparison Analysiscontrasting

confidence: 63%

“…Comparing the PSNR values of different algorithms in the same shaded database in Table 3, it can be found that the value of the RSnet model was higher than other algorithms, especially in the RSDB database proposed in this paper, and the PSNR value was higher. The effect of the shadow removal model proposed by Yu [21] was similar to that of Gong [15]; the model PSNR values proposed by Yang [28] were relatively inferior, and so was the shadow removal performance. Qu [31] proposed an algorithm that had good performance, second only to the model proposed in this paper.…”

Section: Performance Comparison Analysismentioning

confidence: 69%

“…In particular, as can be seen from the last line of Table 2, in the newly created RSDB database, when the SSIM values of other algorithms were compared with the SSIM values obtained by the RSnet model of the algorithm, the SSIM value of the former was lower, the shadow removal effect of the image was poorer, and the value of the RSnet model was much higher than other algorithms. Particularly, in the evaluation of the real ground shadow-free image and the shadow removal image obtained by the algorithm, it can be seen from Table 2 that it was 1.09 times the SSIM value obtained by the shadow removal algorithm proposed by Yu [21], and 1.47 times the SSIM value obtained by the model proposed by Yang [28].…”

Section: Performance Comparison Analysismentioning

confidence: 93%

“…Gong and Cosker used a rough hand-marked form to detect shadows while sacrificing finer shadows and a full range of simpler user input autonomy. Gryka [16] and Yu [21] et al also proposed to manually mark the shadows in an interactive manner to achieve the purpose of shadow removal. Different from Gong and Cosker, Yu [21] proposed a color line-based artificial interactive shadow removal algorithm, which required users to provide shaded areas, shadow-free areas, and areas with similar textures changing significantly in brightness.…”

Section: Related Workmentioning

confidence: 99%

“…Gryka [16] and Yu [21] et al also proposed to manually mark the shadows in an interactive manner to achieve the purpose of shadow removal. Different from Gong and Cosker, Yu [21] proposed a color line-based artificial interactive shadow removal algorithm, which required users to provide shaded areas, shadow-free areas, and areas with similar textures changing significantly in brightness. In the work of Gryka [16], they used the unsupervised regression algorithm to remove the shadows through inputting the shaded area as processed by user, and mainly focused on the soft shadows in the real scene.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks

Fan

Han

2019

Applied Sciences

View full text Add to dashboard Cite

Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization.

show abstract

Section: Performance Comparison Analysiscontrasting

confidence: 63%

Section: Performance Comparison Analysismentioning

confidence: 69%

Section: Performance Comparison Analysismentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks

Fan

Han

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

A Graph-Based Color Lines Model for Image Analysis

Duque-Arias¹,

Velasco-Forero²,

Deschaud³

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

Region Based Hierarchical Modelling for Effective Shadow Removal in Natural Images

Ren

Sun

et al. 2020

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A New Shadow Removal Method Using Color-Lines

Cited by 18 publications

References 22 publications

Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks

Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks

A Graph-Based Color Lines Model for Image Analysis

Region Based Hierarchical Modelling for Effective Shadow Removal in Natural Images

Contact Info

Product

Resources

About