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

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Showing 1391–1400 of 3874 papers

TitleStatusHype
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion ModelsCode0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
ΩSFormer: Dual-Modal Ω-like Super-Resolution Transformer Network for Cross-scale and High-accuracy Terraced Field Vectorization Extraction—0
Perceptually Optimized Super Resolution—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified