Core Concepts
Improving shadow removal methods for the NTIRE 2023 Challenge.
Abstract
The technical report discusses the improvements made by Team IIM TTI for the NTIRE 2023 Image Shadow Removal Challenge. They focused on image alignment, quality loss functions, shadow detection, joint learning, and data augmentation techniques. Their method achieved competitive scores in LPIPS and Mean Opinion Score. The team addressed issues with external camera parameters, perceptual quality limitations, shadow detector application difficulties, independent optimization of detectors and removers, and insufficient data augmentation. They proposed solutions like homography-based image alignment, new loss functions for structure preservation and SSIM, semi-automatic shadow mask annotation, and joint learning of detectors and removers. Their approach showed promising results in refining shadow detection and removal processes.
Stats
Our method achieved scores of 0.196 (3rd out of 19) in LPIPS.
Our method scored 7.44 (3rd out of 19) in the Mean Opinion Score (MOS).
The training time was 60 hours.
The number of parameters used was 55 million.
Runtime on GPU was around 1010 ms using an A100 GPU.
Quotes
"Our method achieved scores of 0.196 (3rd out of 19) in LPIPS."
"Our method scored 7.44 (3rd out of 19) in the Mean Opinion Score (MOS)."