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 1776–1800 of 3874 papers

TitleStatusHype
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Deep Residual Axial Networks—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
Beyond MR Image Harmonization: Resolution Matters Too—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
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
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Image Super-Resolution Using T-Tetromino Pixels—0
Image Superresolution using Scale-Recurrent Dense Network—0
Deep Photo Cropper and Enhancer—0
Image Super-Resolution using Explicit Perceptual Loss—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
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified