Results 31 to 40 of about 390,825 (274)
Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution
IEEE Access, 2021 Single-image super-resolution, i.e., reconstructing a high-resolution image from a low-resolution image, is a critical concern in many computer vision applications. Recent deep learning-based image super-resolution methods employ massive numbers of model
Jun-Ho Choi +3 more
doaj +1 more source
FRESH – FRI-based single-image super-resolution algorithm [PDF]
, 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
core +1 more source
Single-Image Super-Resolution: A Benchmark [PDF]
, 2014 Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics.
Chih-Yuan Yang +2 more
openaire +1 more source
Deep Learning for Single Image Super-Resolution: A Brief Review [PDF]
, 2019 Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.
Liao, Q +5 more
core +2 more sources
Remote Sensing, 2021 Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution ...
Xiaochen Lu +3 more
doaj +1 more source
Sub-pixel resolving optofluidic microscope for on-chip cell imaging [PDF]
, 2010 We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM). The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate ...
Lee, Seung Ah +3 more
core +2 more sources
Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs
Remote Sensing, 2023 High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and ...
Xin Zheng, Zelun Bao, Qian Yin
doaj +1 more source
Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review
IEEE Access, 2023 High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive.
Karansingh Chauhan +7 more
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Generative collaborative networks for single image super-resolution [PDF]
Neurocomputing, 2020 A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists
Mohamed El Amine Seddik +2 more
openaire +3 more sources
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution
IEEE Access, 2023 Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications.
Armin Mehri +2 more
doaj +1 more source