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 2001–2025 of 3874 papers

TitleStatusHype
DL4DS -- Deep Learning for empirical DownScalingCode1
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-ResolutionCode1
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution—0
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution—0
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis—0
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization—0
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network—0
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites—0
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention—0
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive TransformerCode1
Attentive Fine-Grained Structured Sparsity for Image RestorationCode1
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Progressive Training of A Two-Stage Framework for Video RestorationCode1
A New Dataset and Transformer for Stereoscopic Video Super-ResolutionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified