# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 2 Related Work
[27] trained a kernel least-squares support vector machine (LSSVM) to separate shadow and non-shadow regions. It was embedded it into the Markov random field (MRF) framework and pairwise contextual cues were added to further enhance the performance of the region classifier so the detection of shadow area was realized. A shadow removal method based on area re-lighting is raised based on this. On the other hand, for the umbra and penumbra regions in projection, researchers have proposed an image shadow removal algorithm based on convolutional neural network structure (CNN) [14; 18] or intensity domain [9; 11] in deep learning. Both [PERSON] [14] and [PERSON] [18] used convolutional neuralnetworks (CNN) for learning. The former used multiple CNN structures for fusion applications and conditional random field (CRF) for image shadow region predictions, while the latter applied structured label information to the classification process. The two methods were different in the process of extracting and removing shadows. The former used the Bayesian framework, while the latter applied the least-squares method of optimization to perform the final recovery optimization. [PERSON] et al. [9] used the Markov random field to determine the penumbra region, and then created a smoothing model in the shadow region to construct a shadow mask. [PERSON] [11] extracted the shadow mask by considering features such as color shift, texture gradation, and shadow smoothness. Meanwhile, it reserved the texture of the shadow area during the extraction process. Analyzing and summarizing the above methods, we find that the current method of shadow removal can either restore the texture of the shadow area effectively, or neglect the influence of environment and material of the object. Most methods are interactive rather than fully automated, which greatly reduces the efficiency of use. Therefore, the purpose of this paper is to propose an end-to-end, fully automatic network structure to remove image shadow. With the development of artificial intelligence in recent years, some researchers have established fully automatic models for image shadow removal [28; 29; 30; 31]. A shadow removal algorithm based on generative adversarial networks (GANs) is proposed in the work [30], which uses two GANs for joint learning in order to detect and remove shadows. Although this allowed fully automatic shadow detection using GANs, it relied on the shadow detection result of the previous generation against the network in the shadow removal stage, and it did not perform well when dealing with small shadow areas and shadow edges. In the work of [PERSON] [28], a three-dimensional inherent image restoration method based on bilateral filtering and a two-dimensional intrinsic image is proposed. Two-dimensional original images are derived from the original image, and then the shadow restoration method of the three-dimensional original image is proposed by deducing two-dimensional original images and bilateral filtering. Finally, an automatic shadow removal method for a single image is realized. However, since the reconstruction of the image may result in changes in the non-shaded area, a high-quality image consistent with the shadow-free image generally cannot obtained. Therefore, this paper aims to explore the end-to-end deep convolutional neural network for image shadow removal. An encoding-decoding neural network is used, and a small neural network with a two-layer neural network structure is used to train learning. Then, the original image (shadow image) is inputted into the trained network structure to realize fully automatic shadow removal. Finally, a high-quality, shadow-free image is obtained.