Infrared Image Super-Resolution: Systematic Review, and Future Trends
원문 초록
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at \url{https://github.com/yongsongH/Infrared_Image_SR_Survey
함께 살펴볼 논문
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DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
Infrared Image Super-Resolution via Transfer Learning and PSRGAN
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
A Tree-guided CNN for image super-resolution
토론
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