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 301–325 of 3874 papers

TitleStatusHype
A parametric non-negative coupled canonical polyadic decomposition algorithm for hyperspectral super-resolution—0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images—0
Gradient-Free Adversarial Purification with Diffusion Models—0
Binary Diffusion Probabilistic Model—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-ResolutionCode1
FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
Deep learning for temporal super-resolution 4D Flow MRICode0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation—0
C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-ResolutionCode0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis—0
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
Real-Time Neural-Enhancement for Online Cloud Gaming—0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution—0
Super-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation AccuracyCode0
StructSR: Refuse Spurious Details in Real-World Image Super-ResolutionCode1
Bit-depth color recovery via off-the-shelf super-resolution models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified