Traditional photoelectric monitoring is monitored using a large number of identical cameras. In order to ensure the full coverage of the monitoring area, this monitoring method uses more cameras, which leads to more monitoring and repetition areas, and higher costs, resulting in more waste. In order to reduce the monitoring cost and solve the difficult problem of finding, identifying and tracking a low altitude, slow speed and small target, this paper presents spatial vision network for low-slow-small targets recognition. Based on camera imaging principle and monitoring model, spatial vision network is modeled and optimized. Simulation experiment results demonstrate that the proposed method has good performance.
KEYWORDS: Digital signal processing, Field programmable gate arrays, Image processing, Parallel processing, Signal processing, Data processing, Deconvolution, Detection and tracking algorithms, Image restoration, Point spread functions
In this paper, we present a co-design method for parallel image processing accelerator based on DSP and FPGA. DSP is
used as application and operation subsystem to execute the complex operations, and in which the algorithms are
resolving into commands. FPGA is used as co-processing subsystem for regular data-parallel processing, and operation
commands and image data are transmitted to FPGA for processing acceleration. A series of experiments have been
carried out, and up to a half or three quarter time is saved which supports that the proposed accelerator will consume less
time and get better performance than the traditional systems.
A clutter suppression algorithm called the rectification filter with indications of bidirectional local binary patterns (BDLBP-RF) is proposed as a resolution to the problem of detecting dim targets in infrared (IR) image sequences. First of all, a local binary pattern (LBP) operator with properties of grayscale and rotation invariance is introduced in the application of clutter suppression. Each pixel in the image is estimated by its spatial neighbor pixels and the corresponding LBPs in prior and posterior frames. The approach proposed is based on a spatiotemporal process, in which interframe and intraframe properties of the IR image sequence are both taken into account. The method is evaluated by the comparative experiments, and the LBP operator's optimum values of radius and number of neighbors are discussed. The results of the experiment prove that BDLBP-RF has excellent performance and stability in clutter suppression under various situations. The target point in images processed by our approach received high signal-to-clutter ratio gain, and the detectability of the target is enhanced.
In this paper, we consider lossy image compression, which is based on wavelet theory. We introduce image restoration
technology into the wavelet compression. By applying image restoration to the low-frequency component obtained by
entropy decoding in decompression process, we retrieve a gained high-frequency component, which is the expression of
reconstructed image texture in frequency domain. As a benchmark, the algorithm we present is compared to the
traditional wavelet compression. The results of comparative experiments show that our method performs better than
traditional algorithms. The PSNR in our method is elevated generally, and the reconstructed image is more texture-richer
than the traditional approach without restoration.
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