An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. The L2 norm between the two Gaussian mixture models represents the difference in the local grayscale intensity and the richness of the details of the pixel information around the two pixels. In this sense, the L2 norm between Gaussian mixture models can more accurately measure the similarity between pixels. The experimental results show that the proposed method can improve the denoising performance of the images while retaining the detailed information of the image.
With the rapid development of marine exploration and marine transportation, the activities of marine ships are becoming more and more frequent. Accurate and rapid detection of the position of marine ships has very important practical and strategic significance. SAR has the characteristics of all-weather detection. It is an important means of ship detection. Aiming at the problem of fuzzy interference in sea surface ship SAR image detection, a small target image detection algorithm based on constant false alarm rate (CFAR) and depth belief network (DBN) is proposed. Firstly, according to the traditional CFAR detection principle, the whole image to be detected is detected by CFAR globally, and the index matrix is obtained, so as to improve the ship detection speed. Secondly, the output data of the hidden layer of the last layer of DBN are used as the input data of SVM, and the trained DBN model is applied to local detection, so as to improve the accuracy and robustness of ship detection. Finally, the algorithm combining CFAR and DBN is trained and applied in ship detection. Experimental results show that the accuracy of the proposed algorithm under fuzzy interference is better than that of traditional CFAR, BPNN, Fast R-CNN, and SSD512 algorithms, which proves that the robustness of the combined algorithm is significantly improved.
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