ShadowRemovalNet: Efficient Real-Time Shadow Removal
Alzayat Saleh Alex Olsen Jake Wood Bronson Philippa Mostafa Rahimi Azghadi
Shadows significantly impact computer vision tasks, particularly in outdoor
environments. State-of-the-art shadow removal methods are typically too
computationally intensive for real-time image processing on edge hardware. We
propose ShadowRemovalNet, a novel method designed for real-time image
processing on resource-constrained hardware. ShadowRemovalNet achieves
significantly higher frame rates compared to existing methods, making it
suitable for real-time computer vision pipelines like those used in field
robotics. Beyond speed, ShadowRemovalNet offers advantages in efficiency and
simplicity, as it does not require a separate shadow mask during inference.
ShadowRemovalNet also addresses challenges associated with Generative
Adversarial Networks (GANs) for shadow removal, including artefacts, inaccurate
mask estimations, and inconsistent supervision between shadow and boundary
pixels. To address these limitations, we introduce a novel loss function that
substantially reduces shadow removal errors. ShadowRemovalNet's efficiency and
straightforwardness make it a robust and effective solution for real-time
shadow removal in outdoor robotics and edge computing applications.