Single Image Shadow Removal Using Low-Scale Detail Recovering

doi:10.3969/j.issn.0372-2112.2020.07.007

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Single Image Shadow Removal Using Low-Scale Detail Recovering

更新时间：2025-07-08

- Single Image Shadow Removal Using Low-Scale Detail Recovering
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1293-1302(2020)
- 作者机构：
  
  1. 新疆理工学院信息工程系,新疆,阿克苏,843100
  2. 温州大学电气与电子工程学院,浙江,温州,325035
  3. 新疆理工学院信息工程系,新疆,阿克苏,843100
  4. 温州大学电气与电子工程学院,浙江,温州,325035
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.07.007
  CLC： TP391.41
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
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Single Image Shadow Removal Using Low-Scale Detail Recovering[J]. Acta Electronica Sinica, 2020, 48(7): 1293-1302.
DOI：

Single Image Shadow Removal Using Low-Scale Detail Recovering[J]. Acta Electronica Sinica, 2020, 48(7): 1293-1302. DOI： 10.3969/j.issn.0372-2112.2020.07.007.

摘要

为了在光照复杂、纹理丰富的图像上获得更好的去阴影效果，基于生成对抗网络提出了一种新颖的阴影去除方法.首先，所提网络中的阴影检测子网为阴影图像生成阴影掩膜，基于该检测结果提出一种光照敏感的多尺度图像分解方法，在几乎不损失光照信息的同时提取图像纹理信息；然后，蒙版生成子网为分解后的低尺度图像生成相应的蒙版用于去除其中阴影；其次，边界复原子网修复阴影边界实现友好的过渡；最后，使用自适应衰减因子引导图像进行细节恢复以得到纹理丰富的结果.实验结果表明所提方法可以有效地提高阴影去除效果.

Abstract

To obtain a better shadow removal result on images with complex illumination and texture

we proposed a novel approach based on generative adversarial networks. Firstly

shadow mask is generated by the shadow detection sub-net from input shadow image. Based on this detection result

we proposed an illumination sensitive multi-scale image decomposition method to extract the texture information with less or no illumination information loss. Secondly

shadow matte is generated by the matte generation sub-net for the low scale shadow image to remove shadows in it. Thirdly

the shadow boundary are naturally recovered by the boundary completion sub-net. Finally

the shadow removal result is obtained using a detail recovering method guided by adaptive attenuation factor. Experimental results show that the proposed method can improve the removal performance effectively.

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