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Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs...
In order to resolve the poor performance of color-based tracker for the fast rotating object in low-resolution vision, the search region of the tracker needs enlarging to a great extent, and the tracking loop requires setting to several times in very frame. Moreover, the states of the tracked object in high-resolution vision can be estimated from its incomplete observations in low-resolution vision...
If the Kalman filter is effective to enhance the CAMSHIFT tracker in the low-resolution image sequence, the state-space model (SSM) that the Kalman filter is based on needs satisfying three stochastic conditions: (1) the observation noise is relatively small; (2) the state noise and observation noise are independent to each other; (3) the distribution of the observation noise is near to Gaussian distribution...
In this paper, a fast view scheme of mass remote sensing images based on image pyramid is described and implemented. The key techniques in this scheme are tiling, image pyramid, and cache scheduling management. This scheme employs tiling to break the limit of the Windows process memory and improve the I/O speed of hard disk. A wavelet based image pyramid is built for multi-resolution displaying. Cache...
A novel image restoration method is proposed to resolve a problem that the traditional restoration method performs poorly when the kind of image degradation model from high- to low-resolution is unconfirmed. In this paper, the proposed method includes a conceptual frame of state space model (SSM) in order to achieve a general model for accurately estimating the high-resolution image sequence from...
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