Mr. Hong Dai Profile

Hong Dai

at XIOPM CAS

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Proceedings Article | 23 November 2024 Paper

Super-resolution reconstruction for low-light imaging using intensified CMOS camera: SNR analysis and algorithm improvement

Hui Zhao, Bo Zhang, Hong Dai, Xuewu Fan, Chao Gao, Ge Zhang, Yinqiang Su, Zhe Bai, Baopeng Li, Mingyang Yang

Proceedings Volume 13239, 132391M (2024) https://doi.org/10.1117/12.3037336

KEYWORDS: Signal to noise ratio, Reconstruction algorithms, Super resolution, Image restoration, Modulation transfer functions, Cameras, Image enhancement, Microchannel plates, CMOS cameras, Image resolution

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It is difficult for traditional CMOS camera to obtain clear images under extremely low-light conditions for example the new moon or the quarter moon because the photons generated are so few that the signal-to-noise ratio (SNR) is much lower than what is necessary to resolve finer details. Being different from traditional CMOS camera, intensified CMOS, named as ICMOS camera can greatly amplify the very limited arriving photons through external photoelectric effect and thus the corresponding SNR could be improved a lot for low-light conditions. In previous studies, by fusing a series of low-light images having sub-pixel displacement between each other through classical iterative back projection (IBP) reconstruction algorithm, not only the resolution is enhanced but also the SNR increases as well. However why the SNR can be improved through super-resolution reconstruction is not theoretically answered yet. Therefore in this manuscript two contributions are made. In the first place, the characteristics of sub-pixel super-resolution low-light imaging are firstly further investigated. By introducing the concept of spectral SNR, the analytical expression of the SNR before and after super-resolution reconstruction is established, based on which it is concluded that the MTF boosting generated by super-resolution reconstruction is one important factor that can bring in the SNR increment besides inherent noise reducing characteristic of the super-resolution reconstruction algorithm itself. In the second place, by combing the IBP based super-resolution reconstruction algorithm, the FFT (Fast Fourier Transform) based single image amplification and image enhancement methods together, better reconstruction results could be obtained.

Proceedings Article | 23 November 2024 Paper

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

Hong Dai, Bo Zhang, Hui Zhao, Yinqiang Su, Ximing Zhang, Baopeng Li, Mingyang Yang, Xuewu Fan

Proceedings Volume 13239, 132390A (2024) https://doi.org/10.1117/12.3036367

KEYWORDS: Remote sensing, Image enhancement, Image quality, Image processing

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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.

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