Papers Multi-Frame Super-Resolution
“Multi-Frame Super-Resolution” 태그가 달린 논문 36편 · 필터 해제
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further informat…
Image Super-ResolutionMulti-Frame Super-ResolutionRepresentation LearningSuper-Resolution+1HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers …
De-aliasingEarth ObservationImage RegistrationMulti-Frame Super-Resolution+1Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a…
Motion CompensationMotion EstimationMulti-Frame Super-ResolutionSuper-Resolution+1HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers …
De-aliasingEarth ObservationImage RegistrationImage Super-Resolution+2DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery hav…
Image Super-ResolutionMulti-Frame Super-ResolutionRepresentation LearningSuper-ResolutionHandheld Multi-Frame Super-Resolution
Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to n…
DemosaickingMulti-Frame Super-ResolutionSuper-ResolutionMulti-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically …
Multi-Frame Super-ResolutionSuper-ResolutionWide Activation for Efficient and Accurate Image Super-Resolution
Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
Image Super-ResolutionMulti-Frame Super-ResolutionSuper-ResolutionFrame-Recurrent Video Super-Resolution
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality im…
Motion CompensationMulti-Frame Super-ResolutionSuper-ResolutionVideo Super-ResolutionA Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips
We propose a variational approach to obtain super-resolution images from multiple low-resolution frames extracted from video clips. First the displacement between the low-resolution frames and the reference frame are com…
Multi-Frame Super-ResolutionOptical Flow EstimationSuper-ResolutionLight Field Super-Resolution Via Graph-Based Regularization
Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimatio…
Depth EstimationDisparity EstimationMulti-Frame Super-ResolutionSuper-ResolutionConfidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Motion estimation across low-resolution frames and the reconstruction of high-resolution images are two coupled subproblems of multi-frame super-resolution. This paper introduces a new joint optimization approach for mot…
Image ReconstructionMotion EstimationMulti-Frame Super-ResolutionSuper-ResolutionDouble Sparse Multi-Frame Image Super Resolution
A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution. In multi-frame super resolution based on the sparse coding, both …
Image RegistrationImage Super-ResolutionMulti-Frame Super-ResolutionSuper-ResolutionBidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
Super resolving a low-resolution video is usually handled by either single-image super-resolution (SR) or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependen…
Image Super-ResolutionMulti-Frame Super-ResolutionOptical Flow EstimationSuper-Resolution+2Handling Motion Blur in Multi-Frame Super-Resolution
Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide resi…
Image ReconstructionMulti-Frame Super-ResolutionSuper-ResolutionSparse Coding Approach for Multi-Frame Image Super Resolution
An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and spar…
Image Super-ResolutionMulti-Frame Super-ResolutionSuper-Resolution