Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net
arXiv:2604.11071v3 Announce Type: replace-cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fewer parameters than existing methods. Our approach combines frozen algorithm-based preprocessing with a compact U-Net built entirely from depthwise-separable convolutions. The preprocessing normalizes the input distribution by providing complementary brightness-corrected views, enabling the trainable network to focus on residual color correction. Our method achieved 3rd place in the CVPR 2
The paper was published on arXiv, indicating a current development in efficient AI model design, likely driven by ongoing research efforts to optimize AI performance for resource-constrained environments.
This development allows for high-quality image enhancement with significantly fewer computational resources, broadening AI application to devices with limited power or memory and reducing the cost of deployment.
The ability to deploy sophisticated image enhancement algorithms on more edge devices or with less energy consumption alters the economic equation for certain AI vision tasks, making them more accessible and economical.
- · Edge AI device manufacturers
- · Consumer electronics industry
- · Surveillance technology providers
- · AI developers focused on optimization
- · Developers relying solely on high-computation models
- · Cloud-based image processing services (for some use cases)
Increased adoption of AI image enhancement in resource-limited applications and consumer products.
Reduced operational costs and energy footprint for AI vision tasks across various industries.
Acceleration of AI integration into a wider array of embedded systems and IoT devices, potentially fostering new markets.
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Read at arXiv cs.LG