ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
arXiv:1908.01323
Abstract
In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive steps. At each step a shadow attention detector is firstly exploited to generate an attention map which specifies shadow regions in the input image.Given the attention map, a negative residual by a shadow remover encoder will recover a shadow-lighter or even a shadow-free image. A discriminator is designed to classify whether the output image in the last progressive step is real or fake. Moreover, ARGAN is suitable to be trained with a semi-supervised strategy to make full use of sufficient unsupervised data. The experiments on four public datasets have demonstrated that our ARGAN is robust to detect both simple and complex shadows and to produce more realistic shadow removal results. It outperforms the state-of-the-art methods, especially in detail of recovering shadow areas.
The paper was accepted to the IEEE / CVF International Conference on Computer Vision (ICCV) 2019
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Hierarchical Question-Image Co-Attention for Visual Question Answering
- Learning Intrinsic Image Decomposition from Watching the World
- Dynamic Meta-Embeddings for Improved Sentence Representations
Cited by in corpus (5)
- Unsupervised Portrait Shadow Removal via Generative Priors
- Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
- RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
- Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
- Luminance Attentive Networks for HDR Image and Panorama Reconstruction