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The proposed image super-resolution algorithm is summarized in Algorithm 3: The proposed image super resolution scheme is tested on various images.
This paper develops a Bayesian dictionary learning method for hyperspectral image super resolution in the presence of mixed Poisson Gaussian noise.
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Figure 18 Imaging with Galileo and an airborne receiver: (a) Satellite photograph of imaging area, (b) experimental radar image super-imposed on imaging scene.
Image super-resolution has wide applications in biomedical imaging, computer vision, image recognition, etc.
Speedo may have history in its corner and a diverse range of swimwear under its belt but its namesake yields but one defining image: super-tight trunks.
Image super-resolution (SR) has attracted great attention due to its wide practical applications.
We propose a novel, learning-based algorithm for image super-resolution.
Example-based super-resolution is a promising approach to solving the image super-resolution problem.
In recent years, many studies have been conducted on image super-resolution (SR).
Most recent learning-based image super-resolution (SR) methods have attracted much interest.
Recently, deep neural networks have made significant breakthroughs in the image super-resolution (SR) field.
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