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 401–425 of 3874 papers

TitleStatusHype
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
LocalSR: Image Super-Resolution in Local Region—0
Deep priors for satellite image restoration with accurate uncertainties—0
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image—0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
TASR: Timestep-Aware Diffusion Model for Image Super-ResolutionCode1
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA ApproachCode4
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial TranscriptomicsCode1
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion ModelsCode0
VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion ModelsCode1
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
Vision Mamba Distillation for Low-resolution Fine-grained Image ClassificationCode1
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified