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In this paper, we propose an improved non-blind deblurring algorithm based on learning patch likelihoods. However the cost function of this method is quite time-consuming and the method may also produce ringing artifacts. Patch-based method not only uses the valid information of the input image itself, but also utilizes the prior information of the sample images to improve the adaptiveness. These methods usually have the defects of insufficient prior information and relatively poor adaptiveness. Most recent image deblurring methods only use valid information found in input image as the clue to fill the deblurring region. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%. The results show that our model provides superior performance compared with classical visual models. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. The model draws the basic framework of classical ITTI model selects and extracts the texture features and other features that can describe the SAR image better. Then a visual attention model designed for SAR images is proposed. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. However, computational modeling of SAR image scene still remains a challenge. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. A high probability of correct detection (greater than 90%) with a low false alarm rate is achieved. The proposed system is successfully tested on several hundred single-frame IR images that contain multiple examples of military vehicles, with various sizes and brightness levels and in various background scenes and orientations. We suggest a unique variance-based metric for discriminating targets from clutter and for evaluating the probability of correct detection. Geometric and statistical features are automatically extracted for each suspected ROI. The Bayes decision rule is applied for a bimodal histogram while entropic correlation is used for all other cases. Two locally adaptive histogram-based segmentation techniques are applied to extract the target signature. An automatic ROI extraction approach based on localized texture and statistical features is used to locate targets in an IR scene without any prior knowledge of their type, exact size, and orientation. The proposed algorithm requires no templates or a priori knowledge of the targets. Saving Earth Britannica Presents Earth’s To-Do List for the 21st Century.We present a region-of-interest-based segmentation (ROI-S) algorithm and apply it for automatic target detection.Britannica Beyond We’ve created a new place where questions are at the center of learning.100 Women Britannica celebrates the centennial of the Nineteenth Amendment, highlighting suffragists and history-making politicians.

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