Terahertz image denoising via multiscale hybrid-convolution residual network

Terahertz imaging technology has great potential applications in areas, such as remote sensing, navigation, security checks, and so on. However, terahertz images usually have the problems of heavy noises and low resolution. Previous terahertz image denoising methods are mainly based on traditional image processing methods, which have limited denoising effects on the terahertz noise. Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images. Here, a residual-learning-based multiscale hybrid-convolution residual network (MHRNet) is proposed for terahertz image denoising, which can remove noises while preserving detail features in terahertz images. Specifically, a multiscale hybrid-convolution residual block (MHRB) is designed to extract rich detail features and local prediction residual noise from terahertz images. Specifically, MHRB is a residual structure composed of a multiscale dilated convolution block, a bottleneck layer, and a multiscale convolution block. MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising. Ablation studies are performed to validate the effectiveness of MHRB. A series of experiments are conducted on the public terahertz image datasets. The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images. Compared with existing methods, MHRNet achieves comprehensive competitive results.