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您的检索式：作者名="Yunhao GENG"
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| 1 | Learning hyperspectral images from RGB images via a coarse-to-fine CNN显示文摘Hyperspectral remote sensing is well-known for its extraordinary spectral distinguishability to discriminate different materials.However,the cost of hyperspectral image(HSI)acquisition is much higher compared to traditional RGB imaging.In addition,spatial and temporal resolutions are sacrificed to obtain very high spectral resolution owing to the limitations of sensor technologies.Therefore,in this paper,HSIs are reconstructed using easily acquired RGB images and a convolutional neural network(CNN).As a result,high spatial and temporal resolution RGB images can be inherited to HSIs.Specifically,a two-stage CNN,referred to as the spectral super-resolution network(SSR-Net),is designed to learn the transformation model between RGB images and HSIs from training data,including a band prediction network(BP-Net)to estimate hyperspectral bands from RGB images and a refinement network(RF-Net)to further reduce spectral distortion in the band prediction step.As a result,the learned joint features in the proposed SSR-Net can directly predict HSIs from their corresponding scenes in RGB images without prior knowledge.Experimental results obtained on several benchmark datasets demonstrate that the proposed SSR-Net outperforms several state-of-the-art methods by ensuring higher quality in HSI reconstruction,and significantly improves the performance of traditional RGB images in classification. | Shaohui MEI Yunhao GENG Junhui HOU Qian DU | 2022 | Science China(Information Sciences)2022，65，5： | 3 |
| 2 | Fractional-order Sparse Representation for Image Denoising显示文摘Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency. | Leilei Geng Zexuan Ji Yunhao Yuan Yilong Yin | 2018 | IEEE/CAA Journal of Automatica Sinica2018，5，2： | 1 |
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