# Document Image Shadow Removal Based on Illumination Correction Method
## 2 Related Works
### Document Shadow Removal Methods  
[PERSON] et al. [20] proposed the background estimation document shadow removal network (BEDSR-Net). As the first deep network designed for shadow removal in document images, BEDSR-Net contains two modules: background estimation network (BENet) and shadow removal network (SRNet). By learning the global background color and spatial distribution of documents, BENet provides accurate background information and attention map for SRNet to effectively restore shadow-free images. The average peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) on both synthetic and real datasets are 37.55 and 0.9534, respectively. This method needs to calculate two complex neural network models and can only remove lighter shadows. It is not good for complex scenarios such as those with deeper shadows.  
[PERSON] et al. [21] proposed a novel transformer-based model, named ShadocNet, specifically for shadow removal in document images. The ShadocNet mainly consists of three parts: firstly, the shadow detection module extracts a shadow mask on the document image; secondly, the global shadow remapping module uses the visual converter to remap the shadow area color; finally, the RefineNet improves the visual quality by refining the image pixel by pixel. The experimental results show that ShadocNet outperforms previous methods in terms of aesthetic quality. It effectively remaps shadow region colors and refines images, producing fewer artifacts and being closer to real shadow-free images. The PSNR/SSIM/RMSE (root mean square error) of ShadocNet on the Jung and [PERSON] datasets are 24.60/0.91/15.30 and 26.20/0.94/13.48, respectively. However, ShadocNet faces challenges in handling highly complex shadow patterns or insufficiently represented variable lighting conditions.  
[PERSON] et al. [22] proposed an innovative shadow removal method for document images, which pays special attention to color-aware background guidance. Its core technology includes two main aspects: color-aware background extraction network (CBENet) and background-guided shadow removal network (BGShadowNet). The CBENet extracts background images that can accurately reflect variations in the document background color space. The BGShadowNet first generates preliminary shadow removal results using a background-constrained decoder and then performs further refinement through a background-based attention module and a detail improvement module to enhance the textural details. Its PSNR/SSIM/RMSE on the [PERSON] and Kligler datasets are 37.585/0.983/2.219 and 29.176/0.948/5.377, respectively. The results show the advantage of preserving image details and textures. However, when the images are corrupted by heavy noise, outputs may contain some residual noise, resulting in uneven illumination with the surrounding environment.  
These methods utilize sophisticated network architectures to meticulously capture and process shadows. Although they typically work well in effectively removing shadows, they merely leverage the powerful fitting capability of convolutional neural networks while neglecting the optical principles underlying shadow formation.