SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3026–3050 of 3874 papers

TitleStatusHype
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
A deep primal-dual proximal network for image restoration—0
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANsCode0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Deep Learning for Cornea Microscopy Blind DeblurringCode0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling—0
Progressively Unfreezing Perceptual GAN—0
What's in the Image? Explorable Decoding of Compressed Images—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Residual Feature Aggregation Network for Image Super-Resolution—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Zoom in to the details of human-centric videos—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified