The neighbor-embedding (NE) algorithm for single-image super-resolution (SR) reconstruction assumes that the feature spaces of low-resolution (LR) and high-resolution (HR) patches are locally isometric. However, this is not true for SR because of one-to-many mappings between LR and HR patches. To overcome or at least to reduce the problem for NE-based SR reconstruction, we apply a joint learning technique to train two projection matrices simultaneously and to map the original LR and HR feature spaces onto a unified feature subspace. Subsequently, the k -nearest neighbor selection of the input LR image patches is conducted in the unified feature subspace to estimate the reconstruction weights. To handle a large number of samples, joint learning locally exploits a coupled constraint by linking the LR-HR counterparts together with the K-nearest grouping patch pairs. In order to refine further the initial SR estimate, we impose a global reconstruction constraint on the SR outcome based on the maximum a posteriori framework. Preliminary experiments suggest that the proposed algorithm outperforms NE-related baselines. 相似文献
Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. In the first process, neighbor search is performed using the Euclidean distance metric, and in the second process, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. In this paper, we propose a sparse neighbor selection scheme for SR reconstruction. We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the k-nearest neighbor (k-NN) for reconstruction should have similar local geometric structures based on clustering, we employ a local statistical feature, namely histograms of oriented gradients (HoG) of low-resolution (LR) image patches, to perform such clustering. By conveying local structural information of HoG in the synthesis stage, the k-NN of each LR input patch is adaptively chosen from their associated subset, which significantly improves the speed of synthesizing the HR image while preserving the quality of reconstruction. Experimental results suggest that the proposed method can achieve competitive SR quality compared with other state-of-the-art baselines. 相似文献
A multicolor photodetector based on the heterojunction of n‐Si(111)/TiO2 nanorod arrays responding to both ultraviolet (UV) and visible light is developed by utilizing interface engineering. The photodetector is fabricated via a consecutive process including chemical etching, magnetron sputtering, hydrothermal growth, and assembling. Under a small reverse bias (from 0 to ≈?2 V), only the photogenerated electrons in TiO2 are possible to tunnel through the low barrier of ΔEC, and the device only responses to UV light; as the reverse bias increases, the photogenerated holes in Si also begin to tunnel through the high barrier of ΔEV. As a result, the device is demonstrated to have the capacity to detect both UV and visible lights, which is useful in the fields of rapid detection and multicolor imaging. It has been also observed that the crystal orientation of Si affects the characteristics of bias‐controlled spectral response of the n‐Si/TiO2 heterojunctions. 相似文献
Classical linear discriminant analysis (LDA) has been applied to machine learning and pattern recognition successfully, and many variants based on LDA are proposed. However, the traditional LDA has several disadvantages as follows: Firstly, since the features selected by feature selection have good interpretability, LDA has poor performance in feature selection. Secondly, there are many redundant features or noisy data in the original data, but LDA has poor robustness to noisy data and outliers. Lastly, LDA only utilizes the global discriminant information, without consideration for the local discriminant structure. In order to overcome the above problems, we present a robust sparse manifold discriminant analysis (RSMDA) method. In RSMDA, by introducing the L2,1 norm, the most discriminant features can be selected for discriminant analysis. Meanwhile, the local manifold structure is used to capture the local discriminant information of the original data. Due to the introduction of L2,1 constraints and local discriminant information, the proposed method has excellent robustness to noisy data and has the potential to perform better than other methods. A large number of experiments on different data sets have proved the good effectiveness of RSMDA.
压水堆核电厂运行过程中可能发生燃料棒破损。燃料棒一旦破损,所包容的高水平放射性碘等裂变气体将释放至一回路,并可能进一步释放到厂房导致较高的空气污染,增加工作人员受到内照射的风险。对VVER机组燃料棒破损可能导致的碘危害进行了估算和分析,结果表明:即使1根燃料棒破损也可导致大修期间堆厂房放射性碘空气污染水平高达84DAC(derived air concentration)。结合电厂实践从一回路净化除碘、控制碘向厂房空气释放和扩散、空气净化和个人防护等方面探讨了放射性碘危害的控制和防护措施,并提出了后续应对类似情况的建议。 相似文献