Image shadow detection and removal can effectively recover image information lost in the image due to the existence of shadows, which helps improve the accuracy of object detection, segmentation and tracking. Thus, aiming at the problem of the scale of the shadow in the image, and the inconsistency of the shadowed area with the original non-shadowed area after the shadow is removed, the multi-scale and global feature (MSGF) is used in the proposed method, combined with the non-local network and dense dilated convolution pyramid pooling network. Besides, aiming at the problem of inaccurate detection of weak shadows and complicated shape shadows in existing methods, the direction feature (DF) module is adopted to enhance the features of the shadow areas, thereby improving shadow segmentation accuracy. Based on the above two methods, an end-to-end shadow detection and removal network SDRNet is proposed. SDRNet completes the task of sharing two feature heights in a unified network without adding additional calculations. Experimental results on the two public datasets ISDT and SBU demonstrate that the proposed method achieves more than 10% improvement in the BER index for shadow detection and the RMSE index for shadow removal, which proves that the proposed SDRNet based on the MSGF module and DF module can achieve the best results compared with other existing methods.

图像阴影检测和去除可以有效地恢复由于阴影的存在而丢失在图像中的图像信息，从而有助于提高物体检测，分割和跟踪的准确性。因此，针对图像中阴影的比例以及阴影去除后阴影区域与原始非阴影区域不一致的问题，在图像中使用了多尺度和全局特征（MSGF）提出的方法，结合非局部网络和稠密的卷积金字塔池网络。此外，针对现有方法中检测弱阴影和形状复杂的阴影的问题，采用方向特征（DF）模块来增强阴影区域的特征，从而提高了阴影分割的准确性。基于以上两种方法，提出了一种端到端的阴影检测和去除网络SDRNet。SDRNet完成了在统一网络中共享两个特征高度的任务，而无需添加其他计算。在两个公共数据集ISDT和SBU上的实验结果表明，该方法在阴影检测的BER指数和阴影去除的RMSE指数方面实现了10％以上的改进，这证明了该建议的基于MSGF模块和DF模块的SDRNet与其他现有方法相比，可以达到最佳效果。