Prof. Wei Li Profile

Prof. Wei Li

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Proceedings Article | 30 April 2024 Paper

TV-enhanced deep unfolding network for multispectral image demosaicing

Haihao Zhang, Yixiao Yang, Meng Lv, Wei Li, Ran Tao

Proceedings Volume 13155, 131550Z (2024) https://doi.org/10.1117/12.3018618

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Multispectral image (MSI) contains a wealth of spatial information as well as spectral information, making it useful in the application of remote sensing, medical sciences, and beyond. However, traditional scanning-based imaging method is limited to low spatial or temporal resolution. Consequently, the reconstruction of high-resolution, clean, and complete MSI serves as an initial process for the numerous applications. This paper presents a novel deep unfolding network for demosaicing spectral mosaic images obtained through multispectral filter array (MSFA) imaging sensors. Concretely, the proposed network is unfolded from an iterative optimization process into an end-to-end training network, which can efficiently integrate the MSFA-based inherent degradation model with the powerful representation capability of deep neural networks. To further improve performance, a total-variation (TV) denoiser is plugged into the proposed network. Through end-to-end training, the hyperparameters within the optimization framework and TV denoiser are jointly optimized with the parameters of the neural network. Simulation results on CAVE and WHU-OHS datasets show that the proposed method outperforms state-of-the-art methods and improves the generalization capabilities to different MSFA settings.

Proceedings Article | 6 August 2021 Paper

Modified SLIC segmentation for medical hyperspectral cell images

Tingting Qiao, Meng Lv, Wei Li, Yuxing Guo, Xianbo Qiu

Proceedings Volume 11913, 119130K (2021) https://doi.org/10.1117/12.2604859

KEYWORDS: Image segmentation, Hyperspectral imaging, Near infrared, Blood, Principal component analysis, Medical imaging, Image processing, Feature extraction, Distance measurement, Signal processing

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Simple linear iterative clustering (SLIC) is a fast and effective method for superpixel segmentation. However, the similarity measurement method of typical SLIC based on spatial and spectral features fails to get precise segmentation boundaries, especially for the images with complex and irregular shapes. To address this issue, a modified SLIC (MSLIC) method based on spectral, color, and texture information is proposed for medical hyperspectral cell images. The Gabor filter is used to exploit detailed texture features, which processes the image by using signal Fourier transform in the frequency domain. The MSLIC employs normalization, Gamma correction, and principal component analysis (PCA) to preprocess medical hyperspectral images, in which the texture features are integrated with spectral and spatial features to measure the distance. The under-segmentation error and boundary recall are used as the criterion of segmentation. Experiments for two medical datasets indicate that MSLIC achieves better segmentation performance than the typical SLIC method.

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