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Terahertz Image Restoration Benchmarking Dataset

The paper introduces a new terahertz (THz) image benchmarking dataset for THz imaging. The degradation of THz image quality is one of the main problems caused by system noise, intrinsic long-wavelength, and diffraction phenomena. In this paper, the point spread function of the THz imaging process is reconstructed firstly. The THz datasets with ground-truth and degraded images are then synthesized using the point spread function (PSF). We propose a Dense Instantiation Normalization Block (DIN Block) to reconstruct clean THz images. Based on the DIN block, a powerful multi-stage network is designed, named as DINet. DINet achieves the state-of-the-art (SOTA) restoration performance on image rain removal datasets and the proposed THz datasets. To the best of our knowledge, the THz image benchmarking dataset is the first public dataset, which is available at https://github.com/hellogry/THzDatasets