In order to preserve the texture and edge information and to improve the space resolution of single frame, a superresolution algorithm based on Contourlet (NSCT) is proposed. The original low resolution image is transformed by NSCT, and the directional sub-band coefficients of the transform domain are obtained. According to the scale factor, the high frequency sub-band coefficients are amplified by the interpolation method based on the edge direction to the desired resolution. For high frequency sub-band coefficients with noise and weak targets, Bayesian shrinkage is used to calculate the threshold value. The coefficients below the threshold are determined by the correlation among the sub-bands of the same scale to determine whether it is noise and de-noising. The anisotropic diffusion filter is used to effectively enhance the weak target in the low contrast region of the target and background. Finally, the high-frequency sub-band is amplified by the bilinear interpolation method to the desired resolution, and then combined with the high-frequency subband coefficients after de-noising and small target enhancement, the NSCT inverse transform is used to obtain the desired resolution image. In order to verify the effectiveness of the proposed algorithm, the proposed algorithm and several common image reconstruction methods are used to test the synthetic image, motion blurred image and hyperspectral image, the experimental results show that compared with the traditional single resolution algorithm, the proposed algorithm can obtain smooth edges and good texture features, and the reconstructed image structure is well preserved and the noise is suppressed to some extent.
Driver fatigue is a significant factor in many traffic accidents. In this paper, a novel approach is proposed to recognize driver fatigue. First of all, in order to extract effective feature of fatigue expression from face images, supervised locality preserving projections (SLPP) is adopted, which can solve the problem that LPP ignores the within-class local structure by adopting prior class label information. And then multiple kernels support vector machines (MKSVM) is employed to recognizing fatigue expression, Compared to SVM, which can improve the interpretability of decision function and
performance of fatigue recognition. Experimental results are shown to demonstrate the effectiveness of the proposed method.
This paper presents the design and simulation of a novel acceleration sensor with high accuracy and overload ability. A super-stable structure with quad-beams , which has highly symmetric structure has been designed, and this help to eliminate the errors caused by the change of the dimensions and position of the piezoresistors in structure. At the same time, this structure induces films between the beams to reduce the cross-axis sensitivity. Some holes are made in the films to reduce the vertical rigidity. Thus, the films have little effect on the sensitivity. Besides, the sandwich structure is adopted In this device , the damping of the device is controlled by adjusting the clearances between the caps and the seismic mass ,which can obtain the large bandwidth and good frequency response. The bumps are made on two caps to get high overload ability. The piezoresistors are covered with metal layer to improve the electric performance. The structure made beneficial to the high resolution, low cross-axis sensitivity, high overload and good electric performance of the device.
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