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 2151–2200 of 3874 papers

TitleStatusHype
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution—0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Little Pilot is Needed for Channel Estimation with Integrated Super-Resolution Sensing and Communication—0
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer—0
LLV-FSR: Exploiting Large Language-Vision Prior for Face Super-resolution—0
Training a Task-Specific Image Reconstruction Loss—0
Cutting-Edge Techniques for Depth Map Super-Resolution—0
CUF: Continuous Upsampling Filters—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
CT Super Resolution via Zero Shot Learning—0
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)—0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution—0
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting—0
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution—0
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data—0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
LocalSR: Image Super-Resolution in Local Region—0
Local Statistics for Generative Image Detection—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow—0
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair—0
CT-image Super Resolution Using 3D Convolutional Neural Network—0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Low-Resolution Action Recognition for Tiny Actions Challenge—0
Low-Resolution Face Recognition—0
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques—0
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture—0
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification—0
Low Resource Video Super-resolution using Memory and Residual Deformable Convolutions—0
Training Set Effect on Super Resolution for Automated Target Recognition—0
LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network—0
LSR: A Light-Weight Super-Resolution Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified