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

Showing 76–100 of 3874 papers

TitleStatusHype
Super-Resolution with Structured Motion—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors—0
Blind Restoration of High-Resolution Ultrasound Video—0
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?—0
Trustworthy Image Super-Resolution via Generative PseudoinverseCode0
CLIP-aware Domain-Adaptive Super-Resolution—0
Redefining Neural Operators in d+1 Dimensions—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable WeightsCode0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Depth Anything with Any Prior—0
SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds—0
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution—0
Super-Resolution Generative Adversarial Networks based Video Enhancement—0
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
N^2LoS: Single-Tag mmWave Backscatter for Robust Non-Line-of-Sight Localization—0
Revealing economic facts: LLMs know more than they say—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified