Dr. Ximing Zhang Profile

Dr. Ximing Zhang

at XIOPM CAS

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Publications (2)

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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.

Proceedings Article | 23 January 2023 Paper

Infrared small target detection algorithm based on entropy weighted multiscale local contrast

Jingbo Wei, Rongli Chen, Ximing Zhang, Hui Zhao

Proceedings Volume 12557, 125570U (2023) https://doi.org/10.1117/12.2648172

KEYWORDS: Infrared radiation, Detection and tracking algorithms, Infrared imaging, Infrared detectors, Target detection, Image segmentation, Image processing, Clouds, Visualization, Signal to noise ratio

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It is an important and challenging topic to deal with infrared small target detection with high detection rate, low false alarm rate and low computational complexity in various application fields. Aiming at solving the problem that the existing algorithms can not effectively enhance the real foreground target and suppress various complex background interference. This paper proposes an infrared small target detection algorithm based on entropy weighted multiscale local contrast. Firstly, the local contrast formula is redefined in the joint form of ratio and difference, which can enhance the target and suppress the background clutter. Secondly, the local entropy can be used to reflect the gray mutation in the local area of the image. We use the modified local entropy operator to weight the multiscale local contrast. Finally, we employ the adaptive threshold segmentation to separate the target from the background and obtain the final infrared small target. We test six groups of infrared image sequences with different targets and backgrounds, the backgrounds include mountain, forest, field, sea-sky, sky and thick cloud. Experimental results show that, the proposed algorithm can not only robustly detect infrared dim and small targets of different sizes in various complex backgrounds, but also has higher detection efficiency and lower false alarm rate compared with other traditional baseline methods.

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