To better suppress the reflection layer image by shooting through the glass, we propose a reflection suppression model to highlight the main information of the reflected image. We combine the local linear model of a guided filter with the gradient threshold to enhance the boundary contour of the image to achieve the effect of suppressing reflections and effectively solve the established partial differential equations by using discrete cosine transform. Experiments on images taken in different scenes prove the superiority of this method is the problem of single- image reflection suppression.
For the current problem of small target detection, this paper first sorts out the development and current situation of target detection algorithms, and systematically summarizes the research progress of target detection algorithms on complex ground backgrounds. Secondly, we start with two major categories of hyperspectral small target detection and infrared small target detection, and each category is analyzed from different methods. Then we take the representative algorithm as an example to analyze its detection performance and its application under the actual complex ground background conditions. Finally, we respectively make prospects and predictions for each type of algorithm in the application of complex ground background target detection, which provides a reference for future research on small target detection problems.
This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.
Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.
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