Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

🤖 Plain-English Summary

When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. The user study and application on low-light object detection also reveal the latent practical values of our method.

🔑 Key Findings

• However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process.
• Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional neural networks, showing limitations in capturing long-range dependencies.
• In this paper, we formulate a simple yet principled One-stage Retinex-based Framework (ORF).

🔗 Related Resources