MMFF-NET: Multi-layer and multi-scale feature fusion network for low-light infrared image enhancement

Most existing infrared image enhancement algorithms focus on detail and contrast enhancement of ordinary infrared images, and when applied to low-light infrared images, detail and target texture are often severely lost. The reason is that most algorithms process images in a single scale and have difficulty coping with the degradation of image features while enhancing brightness. To solve this problem, we propose a multi-layer and multi-scale feature fusion network (MMFF-Net). It can improve the brightness of low-light infrared images in the absence of normal-light reference samples and keep the image details consistent with the source image. In this paper, features at different layers of the image are extracted using an adaptively modified deep network. A multi-scale adaptive feature fusion module (MAFFM) is designed to preserve and fuse multi-scale information from different convolutional layer features. The fusion features are passed to the iterative function as pixel-wise parameters for image brightness enhancement. We also propose the local feature fusion module (LFFM), which reconstructs images after fusing multiple features, including brightness enhancement images and source images. Finally, in order to implement the training of the whole network, a set of loss functions is carefully designed in this paper. After extensive experiments, it is shown that the algorithm in this paper can effectively enhance low-light infrared images and perform well in subjective visual tests and quantitative tests compared to existing methods.