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

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

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

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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