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 2201–2225 of 3874 papers

TitleStatusHype
High-Resolution Image Synthesis with Latent Diffusion ModelsCode4
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
On Efficient Transformer-Based Image Pre-training for Low-Level VisionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network—0
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image RecognitionCode1
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity—0
Feature Distillation Interaction Weighting Network for Lightweight Image Super-ResolutionCode1
Stable Long-Term Recurrent Video Super-ResolutionCode1
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution—0
Kernel-aware Burst Blind Super-ResolutionCode0
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Text Gestalt: Stroke-Aware Scene Text Image Super-ResolutionCode1
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation—0
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
Implicit Transformer Network for Screen Content Image Continuous Super-ResolutionCode1
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution—0
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction—0
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
A Survey on Deep learning based Document Image Enhancement—0
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
PP-MSVSR: Multi-Stage Video Super-ResolutionCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified