Wide field-of-view (FOV) and high-resolution (HR) imaging systems have become indispensable information acquisition equipment in many applications, such as video surveillance, target detection and remotely sensed imagery. However, due to the constraints of spatial sampling and detector processing level, the ability of remote sensing to obtain high spatial resolution is limited, especially in the wide FOV imaging. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize super-resolution imaging in a low-light-level (LLL) environment. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, redundant low-frequency signals can pass through the network tail-ends, furthermore, the more important high-frequency components calculation can be focused. The qualitative and quantitative analysis results show that the proposed method achieves the most advanced performance compared with other state-of-the-art methods, which shows the superiority of the design framework and the effectiveness of presenting modules.
The wide application of the image super-resolution algorithms significantly improves the visual quality of infrared images. In this paper, an infrared image super-resolution reconstruction method based on a closed-loop regression network is proposed. The residual channel attention block is introduced into the up-sampling module group, which effectively improves the capacity of the network and increases the number of feature maps, enhances the extraction and recovery ability of infrared image features, and is conducive to the recovery of image details. Compared with other infrared information recovery methods previously proposed, the proposed method has obvious advantages in high-resolution detail acquisition capability. Neural network through closed-loop regression, this scheme overcomes the LR image to HR image defects in nonlinear mapping function, by introducing additional constraints on the LR data to reduce the space of the possible functions.
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