SSDSNet: Dual-stage self-supervised network for low-light image enhancement

Key Points

• Enhanced low-light image quality is achieved through dual-stage processing, providing clearer visuals.
• This self-supervised network architecture significantly improves key metrics in low-light image scenarios.
• The method employs deep learning techniques for robust performance across various low-light conditions.
• Results demonstrate that the model effectively addresses common image enhancement challenges under low-light settings.