# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 2 Related Work  
In recent years, many scholars have analyzed the characteristics of shadows, established shadow generation models, and proposed a number of algorithms for shadow detection and shadow removal. Analyzing the research results comprehensively, it is not difficult to find that the shadow removal algorithm mostly follows these principles: detecting the shadow or marking it manually, then creating models to remove the shadow. [PERSON] and [PERSON] [15] proposed a statistical-based, interactive shadow removal method for shadows in extreme conditions with different shadow softness, irregular shadow shapes, and existing dark textures. First, using the dynamic learning method, two users roughly marked the shadow and non-shadow areas in the image, meaning the shadow area was highlighted and the non-shadow area was resisted, so the shadow area could be extracted easily. On this basis, the model was constructed for shadow removal. [PERSON] and [PERSON] used a rough hand-marked form to detect shadows while sacrificing finer shadows and a full range of simpler user input autonomy. [PERSON] [16] and [PERSON] [21] et al. also proposed to manually mark the shadows in an interactive manner to achieve the purpose of shadow removal. Different from [PERSON] and [PERSON], [PERSON] [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 [PERSON] [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. [PERSON] [22] proposed a shadow removal algorithm based on [PERSON]'s equation, which had a good effect on a considerable part of the image. However, there was no consideration of the influence of ambient illumination and material changes, thus resulting in poor texture restoration in shadow areas. Generally, the brightness contrast between the shadow area and the non-shadow area in the image are relatively large while the features such as texture and color are similar. Therefore, the shadow area and the non-shadow area have similar gradient fields. [PERSON] et al. [23] used this feature to propose an image shadow removal algorithm based on gradient domain, which solved some shortcomings of Poisson equation, but it had a poor performance on discontinuous or small shadow regions. [PERSON] et al. [24] proposed a shadow removal algorithm based on Subregion Matching Illumination Transfer. This method takes into account the different reflectivity characteristics of different materials, which makes the processing of shadow regions in complex scenes better. On the one hand, [PERSON] [25] proposed a projection shadow detection and elimination algorithm based on the combination of texture features and luminance chrominance chroma (YUV) color space. Firstly, a moving object was detected using a pixel-based adaptive segmenter (PBAS) algorithm that resisted shadows. In addition, a portion of the projected shadow candidate area was obtained by texture detection. Then, another projected shadow candidate area was obtained by shadow detection that was based on the luminance chrominance chroma (YUV) color space. Finally, the two portions of the projected shadow candidate regions were filtered and merged by the shadow features. In the work of [PERSON] [26], they proposed a simple method for detecting and removing shadows from a single red green blue (RGB) image. The shadow detection method was selected based on the average value of the RGB images in the A and B planes of the CIELab color model (LAB) equivalent image, and the shadow removal method was based on the recognition of the amount of light irradiated on the surface. The brightness of the shaded areas in the image was increased, and then the color of the surface portion was corrected to match the bright portion of the surface. [PERSON] et al.