Learning With Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision

Area of Science:

• Computer Vision
• Deep Learning
• Image Processing

Background:

• Low-light images suffer from poor visibility, noise, and color casts, degrading quality and impacting downstream applications.
• Existing deep learning methods for low-light enhancement often require complex architectures and high computational resources.
• A unified framework for diverse low-light vision (LLV) tasks remains a significant challenge.

Purpose of the Study:

• To introduce Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework for LLV.
• To develop an efficient paradigm capable of handling multiple LLV tasks, including enhancement, detection, and segmentation.
• To address the limitations of computational burden and architectural complexity in current low-light image processing techniques.

Main Methods:

• Established a nested optimization formulation combined with an unrolling strategy to explore LLV task principles.
• Designed a differentiable strategy for cooperative architecture search, optimizing for specific scenes and tasks within RUAS.
• Demonstrated the application of RUAS for both low-level (enhancement) and high-level (detection, segmentation) LLV applications.

Main Results:

• RUAS demonstrated flexibility in addressing diverse LLV tasks.
• The framework proved effective in enhancing low-light image quality and performance of downstream tasks.
• Experiments confirmed the efficiency and reduced computational burden of the RUAS approach compared to prior methods.

Conclusions:

• RUAS provides a unified and efficient learning framework for a wide range of low-light vision challenges.
• The architecture search mechanism enables task-specific optimization, enhancing performance and adaptability.
• This research offers a significant advancement in handling degraded images from low-light environments.