We propose a novel network for low-light-level and visible image fusion enhancement task, which is based on the feature extraction convolution neural network. By extracting the high-frequency information of visible light detector under low illumination, and combining the advantages of wide activation network and channel attention mechanism, the network can automatically filter and extract the useful information in the image to complete the super-resolution reconstruction of low light level image. It makes up for the lack of visible light information and low resolution (LR) of low light level detector at night and can realize all-weather real-time imaging. The experimental results show that our method has better numerical performance than the traditional super-resolution network structure, and also retains more abundant image texture information, which is more in line with the feeling of human eyes.
Terahertz waves refer to electromagnetic waves with frequencies ranging from 0.1THz to 10THz.Due to the ability to penetrate many non-polar materials, terahertz waves can be used to detect hidden objects. A convolutional neural network structure called Attention U-Net to achieve super-resolution of terahertz images is proposed in this paper. The function of the convolutional layer and pooling layer in the encoding path is reducing the size and extracting the edge features of the image, while the role of the deconvolution layer in the decoding path is to up-sample the image and restore the image content. The introduction of skip connection on the feature map of the symmetrical encoding path and decoding path maximizes the utilization of feature information in each layer of the network and effectively solves the problem of gradient disappearance. This network also replaces the convolution on the codec path with the attention mechanism block, including the spatial attention mechanism and the channel attention mechanism, which makes the extracted features more directional, obtains more detailed information about the target that needs to be focused and suppresses other Useless information. The network and algorithm proposed in this paper have good results in experiments and have a wide range of application prospects in the field of security inspection.
A terahertz imaging method based on aperture coding is proposed to solve the problems of large pixel size and low resolution of the terahertz imaging detector. The forward model of the terahertz coded incoherent imaging system is established, and the optimal coding imaging strategy is discussed. By adding coded modulation to the aperture, the image detected by the imaging detector can generate pixel-level light intensity conversion. Through the multi-frame aperture coding simulation experiment, the pixel aliasing problem caused by the detector pixel size is effectively solved, and the imaging resolution is improved to the diffraction limit of the approximate lens, so as to guide the future experiments.
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