The minimum spanning tree (MST) stereo-matching method is an information-infiltration process. The difference in edge attributes of an MST can cause the edge-expansion phenomenon, which affects the matching accuracy. To accurately recover image-depth information, a dynamicprogramming stereo-matching method based on the MST was proposed. First, the colour Birchfield Tomasi costcalculation method based on image adaptive colour information was proposed to obtain stable initial cost values.Second, the image was segmented into superpixel regions using the simple linear iterative clustering algorithm. The pixel-and region-level MSTs were then constructed. Next, combined with the idea of dynamic programming, the MST cost-aggregation process was re-deduced. On this basis, the aggregate cost values of the two MSTs were obtained. Finally, the aggregate cost values were combined adaptively to acquire the high-precision smooth disparity map. The Middlebury 2014 dataset was used for the experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of stereo-matching.

Aiming at solving the degradation problem of Luojia 1-01 night-light remote sensing images, the main reason for the “glow” phenomenon was analyzed. The APSF (Atmospheric Point Spread Function) template of night-light image was obtained from atmospheric source scattering. The template was used as the initial value in the regularization restoration model in this paper. Experiments were carried out using single point and regional images. The results demonstrate that the estimated APSF and restoration results of the method are better than those from other methods, and the image quality is improved after restoration.

In this paper, a power-efficient and real-time image feature detecting system is implemented, which is based on the Speeded-Up Robust Feature (SURF) algorithm. We optimized the SURF algorithm, and implemented on the FPGA fabric of Xilinx ZYNQ-7020 device. Our design of SURF algorithm circuit can work up to 100Mhz clock frequency, and its processing speed up to 270 fps for standard VGA (640 * 480) resolution gray image. We implemented the system on the ZYNQ platform with the hardware and software co-design approach. The image feature detecting system based on SURF algorithm circuit runs embedded Linux system. There is a GUI application for Linux system designed with QT and open-cv, which can capture video, process and display image or video. The system meets the real-time and low-power requirements of embedded devices, with great practical value.

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When the matching cost function in Semiglobal Matching is unstable, the inaccurate matching cost values will be propagated in the cost aggregation process. It will lead to a serious mismatching phenomenon. To address the problem, a binocular images dense matching method considering image adaptive color weights and feature points was proposed. Firstly, The Color Birchfield Tomasi (CBT) matching cost calculation method was proposed to obtain a stable initial cost volume, which combined image adaptive color weights and gradient information. Secondly, the Scale-invariant Feature Transform matching algorithm was used to extract the a priori feature points from binocular images. Then, the feature points were filtrated. The cost volume was optimized by using their coordinate information and disparity information. Finally, an aggregation path segmentation rectification method was adopted to optimize the SGM aggregation paths and reduce the propagation of incorrect paths. Experimental results demonstrate that the proposed method can effectively improve the stability and accuracy of dense matching, reduce the mismatching phenomenon, and finally produce high-quality disparity maps.