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 1226–1250 of 3874 papers

TitleStatusHype
Deep priors for satellite image restoration with accurate uncertainties—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions—0
Frame-Recurrent Video Super-Resolution—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
Deep Photo Cropper and Enhancer—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Deep Networks for Image and Video Super-Resolution—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
Deep multi-frame face super-resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified