An infrared image enhancement method via content and detail Two-Stream deep convolutional neural network

Aiming at the problems of low contrast and blurry details of infrared images, a novel two-stream deep full convolutional neural network is proposed for low-quality infrared image enhancement. An infrared detail enhancement sub-network and a global content-invariant sub-network are designed to achieve adaptive enhancement of infrared features. First, the detail enhancement network composed of mixed attention block (MAB) with multi-convolutions, residual learning (RL) and up-sampling unit is used to extract deep features from inputs, learn meaningful thermal radiation target information, restrain unnecessary background, and then perform the separation of target and background. Second, the content-invariant network mainly consisting of dilated convolutions and multi-scale convolutions captures rich contextual information to focus on the overall content, maintain the spatial structure, and avoid over-enhancement of local regions. Finally, the fine-tuning unit fuses the features extracted by the two-stream to complete the element complementation between different mappings and generate high-quality infrared images. Furthermore, experiments on public datasets and self-collected infrared datasets demonstrate that the proposed method outperforms other image enhancement methods not only for image quality on PSNR and SSIM, but also that has better visual quality with less artifact and noise.