Terahertz imaging super-resolution algorithm based on Hilbert spatial curve filling
The performance of radiation sources and detectors currently limits terahertz imaging technology,which still requires further improvement in terms of detail resolution,imaging speed,and noise suppression.This paper proposes a terahertz image super-resolution algorithm based on spatial curve filling.The ViT(Vision Transformer)structure back-bone network is utilized to extract terahertz image features through an attention mechanism.A Hilbert spatial curve is constructed to reconstruct the image according to the feature map using the curve filling method.Lightweight one-di-mensional convolution processing is used for reconstructing image features,while inverse transformation of reconstruct-ed maps restores the image's spatial structure.Finally,pixel reorganization enables up sampling to obtain an output im-age with enhanced object contour and details.Experimental results show that compared with conventional ViT struc-tures,this proposed method improves Peak Signal-to-Noise Ratio(PSNR)by 0.81 dB and Structural Similarity Index(SSIM)by 0.007 4,which effectively inhibits the noise influence on texture and significantly improves the resolution and image quality.
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