This paper describes a new RXDMTD algorithm based on RX anomaly detection for moving weak and small targets in multispectral image sequences. The proposed algorithm can effectively suppress background clutter and at the same time enhance the moving weak and small targets in multispectral and out-of-time image sequences. The complex background intensity between the two multispectral images changes significantly, which makes it difficult to suppress the background and difficult to extract the target. In this paper, the image sequence is first arranged and combined, and then the RX algorithm is used to enhance the target and using the target’s movement suppresses the background. Experimental results show that the algorithm proposed in this paper has achieved good detection results.
With the application of image more and more widely, People put forward higher requirements on the image quality of small objects and details in the image. In recent years, with the development of deep learning, it achieved good results in the research of image super-resolution. In this paper, we proposed EDSRGAN, a single image super-resolution(SISR) algorithm, based on enhanced residual network and the adversarial network. Compared with SRGAN, which is also based on the adversarial network, EDSRGAN can greatly reduce the high-frequency noise contained in the super-resolution(SR) image, and it also leads SRGAN in terms of peak signal to noise ratio and structural similarity evaluation indicators. Although EDSRGAN lagged behind EDSR in terms of peak signal to noise ratio and structural similarity, the SR images generated by EDSRGAN were sharper than EDSR in the object edges and targets details. EDSRGAN could achieve good results in image super-resolution on small targets.
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