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Single image super-resolution with denoising diffusion GANS [PDF]
Scientific ReportsSingle image super-resolution (SISR) refers to the reconstruction from the corresponding low-resolution (LR) image input to a high-resolution (HR) image. However, since a single low-resolution image corresponds to multiple high-resolution images, this is
Heng Xiao +6 more
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FRESH – FRI-based single-image super-resolution algorithm [PDF]
IEEE Transactions on Image Processing, 2016 In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets.
Dragotti, P, Wei, X
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Single Image Super Resolution Using Deep Residual Learning
AI, 2023 Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques.
Moiz Hassan +2 more
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Single Image Super-resolution using Deformable Patches. [PDF]
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 2014 We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive.
Zhu Y, Zhang Y, Yuille AL.
europepmc +4 more sources
Sinogram Interpolation Inspired by Single-Image Super Resolution. [PDF]
J Biotechnol Appl, 2023 Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object.
Christiansen C, Zeng GL.
europepmc +3 more sources
Single Frame Image super Resolution using Learned Directionlets [PDF]
International Journal of Artificial Intelligence & Applications, 2010 In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented.
Learned Directionlets +2 more
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Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
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Image Super-Resolution Reconstruction Method Based on Embeddable Network Structure [PDF]
Jisuanji gongcheng, 2021 The existing Super-Resolution(SR) image reconstruction models based on Convolutional Neural Networks(CNN) have multiple deficiencies,such as instable model training process and low convergence speed.To address the problem,this paper proposes an ...
QIANG Baohua, PANG Yuanchao, YANG Minghao, ZENG Kun, ZHENG Hong, XIE Wu, MO Ye
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Deep Back-ProjectiNetworks for Single Image Super-Resolution [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images.
Muhammad Haris +2 more
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SUPER RESOLUTION FOR SINGLE SATELLITE IMAGE USING A GENERATIVE ADVERSARIAL NETWORK [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research.
R. Li, W. Liu, W. Gong, X. Zhu, X. Wang
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