In contrast to detection in the sky or sea background, infrared small target detection in the near-earth background shows its own particularity and complexity. In this paper, an infrared image preprocessing algorithm using dark image processing and improved K-SVD algorithm is proposed. In the first place, an infrared image model in the near-earth background is constructed. Due to the similar characteristic of low contrast between infrared images and foggy images, we propose an analogical method that analogizes infrared images as foggy images. On this basic, theories of image dehazing can be employed in the process. In this preprocessing algorithm, near-earth background suppression in infrared images is achieved by dark image processing method. After background suppression, an improved K-SVD algorithm based on NLM algorithm is applied for image denoising. Considering relevant information of different image blocks and the orthogonality between residual terms after denoising and chosen atoms, a regularized constraint represent for image self-similarity information is introduced to improve K-SVD algorithm. Experiments show that the proposed preprocessing algorithm can effectively suppress near-earth background, enhance the contrast between target and its peripheral region, and improve the performance of infrared image preprocessing.
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