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 411–420 of 3874 papers

TitleStatusHype
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
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
Vision Mamba Distillation for Low-resolution Fine-grained Image ClassificationCode1
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified