LP-IOANet: Efficient High Resolution Document Shadow Removal
This addresses the need for efficient, high-resolution shadow removal in document enhancement pipelines, particularly for real-time applications on mobile devices, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of document shadow removal to enhance visibility and readability, proposing LP-IOANet, which outperforms state-of-the-art methods by a 35% improvement in mean average error and runs real-time at four times higher resolution on mobile devices.
Document shadow removal is an integral task in document enhancement pipelines, as it improves visibility, readability and thus the overall quality. Assuming that the majority of practical document shadow removal scenarios require real-time, accurate models that can produce high-resolution outputs in-the-wild, we propose Laplacian Pyramid with Input/Output Attention Network (LP-IOANet), a novel pipeline with a lightweight architecture and an upsampling module. Furthermore, we propose three new datasets which cover a wide range of lighting conditions, images, shadow shapes and viewpoints. Our results show that we outperform the state-of-the-art by a 35% relative improvement in mean average error (MAE), while running real-time in four times the resolution (of the state-of-the-art method) on a mobile device.