Single Image Super-Resolution Based on Gradient Profile Sharpness
Abstract of Single Image Super-Resolution Based on Gradient Profile Sharpness
Single Image Super-Resolution Based on Gradient Profile Sharpness. Single image superresolution is a classic and active image processing problem, which aims to generate a high-resolution (HR) image from a low-resolution input image.
Due to the severely under-determined nature of this problem, an effective image prior is necessary to make the problem solvable.
to improve the quality of generated images.
The goal of single image super-resolution is to construct a high resolution (HR) image from a low resolution (LR) image input.
This problem is an classical and active topic in image processing, which is also a crucial step in many practical situations, e.g. image display, remote sensing, medical imaging and so on.However, image super-resolution problem is an inherently ill-posed problem, where many HR images may produce the same LR image when down-sampled. As a result, how to generate an HR image with good visual perception and as similar as its ground truth has become the goal of image super-resolution.
Conclusion
These approaches are more robust, however there are always some artifacts on their super-resolution results. Generally, the computational complexity of learning-based super-resolution approaches is quite high.To make a tradeoff between algorithm performance and algorithm computational efficiency, many reconstruction-based approaches have been proposed over the years.
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