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 1651–1675 of 3874 papers

TitleStatusHype
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
Deep Image Super Resolution via Natural Image Priors—0
High-throughput lensless whole slide imaging via continuous height-varying modulation of tilted sensor—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
Coupled-Projection Residual Network for MRI Super-Resolution—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Improved detection of small objects in road network sequences—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
Improving Generative Adversarial Networks for Video Super-Resolution—0
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts—0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified