Bridging Robustness and Efficiency: Real-Time Low-Light Enhancement via Attention U-Net GAN

• URL: http://arxiv.org/abs/2601.06518v1
• Date: Sat, 10 Jan 2026 10:39:22 GMT
• Title: Bridging Robustness and Efficiency: Real-Time Low-Light Enhancement via Attention U-Net GAN
• Authors: Yash Thesia, Meera Suthar,
• Abstract summary: We propose a hybrid Attention U-Net GAN that provides generative-level texture recovery at edge-deployable speeds.<n>Our method achieves a best-in-class LPIPS score of 0.112 among efficient models.<n>This represents a 40x speedup over latent diffusion models, making our approach suitable for near real-time applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Low-Light Image Enhancement (LLIE) have focused heavily on Diffusion Probabilistic Models, which achieve high perceptual quality but suffer from significant computational latency (often exceeding 2-4 seconds per image). Conversely, traditional CNN-based baselines offer real-time inference but struggle with "over-smoothing," failing to recover fine structural details in extreme low-light conditions. This creates a practical gap in the literature: the lack of a model that provides generative-level texture recovery at edge-deployable speeds. In this paper, we address this trade-off by proposing a hybrid Attention U-Net GAN. We demonstrate that the heavy iterative sampling of diffusion models is not strictly necessary for texture recovery. Instead, by integrating Attention Gates into a lightweight U-Net backbone and training within a conditional adversarial framework, we can approximate the high-frequency fidelity of generative models in a single forward pass. Extensive experiments on the SID dataset show that our method achieves a best-in-class LPIPS score of 0.112 among efficient models, significantly outperforming efficient baselines (SID, EnlightenGAN) while maintaining an inference latency of 0.06s. This represents a 40x speedup over latent diffusion models, making our approach suitable for near real-time applications.

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