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 3001–3050 of 3874 papers

TitleStatusHype
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Learning to Learn to Compress—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Video compression with low complexity CNN-based spatial resolution adaptation—0
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-ResolutionCode0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Deep learning Framework for Mobile MicroscopyCode0
Video Super Resolution Based on Deep Learning: A Comprehensive Survey—0
T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions—0
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Super-Resolution Remote Imaging using Time Encoded Remote Apertures—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
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