Results 21 to 30 of about 10,359,966 (377)
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Computer Vision and Pattern Recognition, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi +7 more
semanticscholar +1 more source
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [PDF]
IEEE International Conference on Computer Vision, 2021 It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
K. Zhang +3 more
semanticscholar +1 more source
Reliable Perceptual Loss Computation for GAN-Based Super-Resolution With Edge Texture Metric
IEEE Access, 2021 Super-resolution (SR) is an ill-posed problem. Generating high-resolution (HR) images from low-resolution (LR) images remains a major challenge. Recently, SR methods based on deep convolutional neural networks (DCN) have been developed with impressive ...
J. Kim, C. Lee
doaj +1 more source
Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
semanticscholar +1 more source
Super resolution for root imaging [PDF]
Applications in Plant Sciences, 2020 PremiseHigh‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection.
Jose F. Ruiz‐Munoz +4 more
openaire +4 more sources
Image Super-Resolution Using Deep Convolutional Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images.
Chao Dong +3 more
semanticscholar +1 more source
Point cloud super‐resolution based on geometric constraints
IET Computer Vision, 2021 Among all digital representations we have for real physical objects, three‐dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing.
Xiaoqiang Li, Jitao Liu, Songmin Dai
doaj +1 more source
Accurate Image Super-Resolution Using Very Deep Convolutional Networks [PDF]
Computer Vision and Pattern Recognition, 2015 We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19].
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
semanticscholar +1 more source
Sensors, 2021 Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed
Ning Zhang +4 more
doaj +1 more source
Image resolution enhancement using dual-tree complex wavelet transform [PDF]
, 2010 In this letter, a complex wavelet-domain image resolution enhancement algorithm based on the estimation of wavelet coefficients is proposed. The method uses a forward and inverse dual-tree complex wavelet transform (DT-CWT) to construct a high-resolution
Tjahjadi, Tardi, Çelik, Turgay
core +1 more source