Single-Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, ...

由 C Ledig 著作 · 2017 · 被引用 12923 次 — Here we will focus on single image super-resolution (SISR) and will not further discuss approaches that recover HR images from multiple images [3, 14].

Single-image super-resolution (SISR) is a computer vision task that reconstructs a high-resolution (HR) image from a low-resolution (LR) image.

由 H He 著作 · 2011 · 被引用 383 次 — We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is ...

由 S Ye 著作 · 2023 · 被引用 7 次 — Single-image super-resolution is an important task in image processing, aiming to reconstruct high-resolution images from low-resolution images and optimize ...

由 CY Yang 著作 · 2014 · 被引用 770 次 — In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative ...

WSISR: Single image super-resolution for Whole slide Imaging using convolutional neural networks and self-supervised color normalization. deep-learning ...

由 H Li 著作 · 2021 · 被引用 387 次 — Abstract:Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, ...

Single-Image-Super-Resolution. A list of resources for example-based single image super-resolution, inspired by Awesome-deep-vision and Awesome Computer Vision ...