Aircraft engine is the main radiation source in the rear hemisphere of combat aircraft, and the main target for infrared guided weapon systems to detect, track, and attack. Therefore, studying the characteristics of aircraft’s backward infrared radiation is the significant basis for infrared stealth design and improvement of plane. In this paper, the principle and method of infrared thermal imaging testing are studied, and the ground static infrared radiation intensity test of a military aircraft is carried out. The relationship between the aircraft’s long-wave and medium-wave infrared Radiation Intensity with time is obtained at the azimuth of 45°, and relevant data is accumulated, which provides technical support for the subsequent aircraft infrared stealth performance assessment.
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.
The sea background video has a wide range of applications in the fields of port maritime traffic management, combating illegal fishing vessels, and maritime rescue. However, the target pixel size in the sea background video is quite small, so increasing the resolution of the target has important practical significance. There are a lot of ripples in the sea background video, which leads to poor video super-resolution effect. We propose a video super-resolution algorithm (CARVSR) in sea background based on the channel attention mechanism. The algorithm adds spatio-temporal 3D learning convolution to the fusion module, which suppresses the interference of ripples on super-resolution reconstruction, and adds channel attention mechanism to the reconstruction module, which enhances the feature expression to reconstruction and improves super-resolution reconstruction quality. Experimental results show that the algorithm effectively improves the superresolution reconstruction effect of sea background video.
In this paper,we propose a new GAN-based end-to-end image fusion network (VIFGAN) for fusion of visible and infrared images. VIFGAN contains a generator and a discriminator. We added the DenseNet module to the generator,and this module can extract deeper features and details. We also propose a Two-way regulation loss function(TWR-Loss). The loss function considers both the radiation information and texture information in the image, which can make the network suitable for image fusion tasks of different spectrum combinations. The experimental results show that in the fusion task of visible light and infrared images, the proposed network has better fusion performance than the existing fusion algorithm, the visual effect of the fused image is better, and the extracted details are more abundant.
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