As an effective way to integrate complementary information in multisensor detection system, image fusion technology has been widely used in robotic vision, medical diagnosis and safety monitoring. At the same time, the dual band infrared detection system has been widely used in the field of guidance and detection.Because dual-band/multi-band infrared detection has the characteristics of wide detection range and multi-target radiation information. Therefore, there is an urgent need of a fusion of the dual-bands infrared images. In order to obtain better image quality, infrared dual-frequency image fusion technology is used to synthesize different radiation information of target and background.In this paper, a new infrared dual-band image fusion with simplified pulse coupled neural network(PCNN) and visual saliency map(VSM) Framework in nonsubampled shearlet domain (NSST) is proposed. In the proposed method, first, the sours images are decomposed into base parts and multiscale and multidirection representations in NSST domain. Then，base parts are fused by VSM fusion approach. For the high-frequency bands are fused by a Simplified pulse coupled neural network model. Finally, the final image is reconstructed by inverse NSST. As a result, the fused image details will be presented more naturally, which is more suitable for human visual perception. The experimental results demonstrate that evaluation quality of the fused images is improved by comparing three objective evaluation factors with three popular fusion methods.This technology is of great significance to the development of image field.
In recent years, small and weak target detection technology is one of the hotspots in information processing technology. However, the detection precision and speed of weak targets still have yet to be improved.
As a branch of machine learning, deep learning has become more and more widely used in various fields. Therefore, this paper improves the deep convolutional networks for the characteristics of weak target detection, including the following three aspects:
Firstly, a dataset dedicated to small and weak target detection is established. The data is sufficient and representative, which is beneficial to improve the quality of the network model. Each image in the dataset has a corresponding label that indicates the name of the image, and the coordinates and width of the target circumscribed rectangle.
Secondly, the image is dilated many times so that the target having only a few pixels is covered by a lot of pixels. The highlighted portion of the image is dilated, and the result image has a larger highlighted area than the original image.
Thirdly, the Faster R CNN algorithm is improved. In this paper, by adjusting the learning rates, a suitable one is determined to get the best network model.
The results show that the average precision on the dataset has improved. The method proposed in this paper is of great significance for the detection of small and weak targets. For the military field, the research on weak target detection has high military value for improving early warning capability and counterattack capability.