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 3551–3575 of 3874 papers

TitleStatusHype
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
DSR-Diff: Depth Map Super-Resolution with Diffusion ModelCode0
A Regularized Conditional GAN for Posterior Sampling in Image Recovery ProblemsCode0
Progressive Face Super-Resolution via Attention to Facial LandmarkCode0
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR VideoCode0
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal CorrelationsCode0
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial NetworksCode0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face HallucinationCode0
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Projected Distribution Loss for Image EnhancementCode0
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimationCode0
Super-Resolving Face Image by Facial Parsing InformationCode0
Joint Super-Resolution and Alignment of Tiny FacesCode0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion ModelCode0
Provably Convergent Plug-and-Play Quasi-Newton MethodsCode0
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying KernelsCode0
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale RefinementCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
Sparsity-based background removal for STORM super-resolution imagesCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified