An algorithm for enhancing low-light remote-sensing images based on zero-reference deep curve estimation (Zero-DCE)

Low-light remote sensing technology is crucial for surface observation during twilight and lunar phases; however, the acquired images often suffer from low contrast, low brightness, and low signal-to-noise ratios, which adversely affect observation quality. Traditional low-light image enhancement algorithms, such as Histogram Equalization, Gamma Correction, and Adaptive Histogram Equalization, can improve visual outcomes but also suffer from issues such as over-enhancement, loss of detail, noise amplification, and insufficient adaptability. To address these limitations, this paper proposes a low-light remote sensing image enhancement method based on Zero-Reference Deep Curve Estimation (Zero-DCE). This approach does not require paired samples and guides network learning through a non-reference loss function, making it particularly suitable for enhancing remote sensing images in low-light environments. Due to the lack of dedicated low-light remote sensing datasets, this study utilizes images from the UCMerced dataset to create simulated low-light remote sensing images for model fine-tuning. All color images are converted to grayscale to align with the characteristics of satellite-based low-light remote sensing images and to simplify the training process. Experimental results demonstrate that the proposed method significantly outperforms traditional techniques in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), while also excelling in denoising and preserving texture authenticity. The optimized Zero-DCE++ not only maintains the original performance but also significantly reduces computational costs and enhances inference speed, which is of great importance for real-time low-light remote sensing image processing on satellite platforms.