arXiv:2312.00689
Infrared Image Super-Resolution via GAN
원문 초록
The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as computer vision and natural language processing. In this chapter, we provide a brief overview of the application of generative models in the domain of infrared (IR) image super-resolution, including a discussion of the various challenges and adversarial training methods employed. We propose potential areas for further investigation and advancement in the application of generative models for IR image super-resolution.
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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