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 1051–1075 of 3874 papers

TitleStatusHype
Sampling Generative NetworksCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionCode1
Image Super-Resolution Using Deep Convolutional NetworksCode1
Generative Adversarial NetworksCode1
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution—0
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolutionCode0
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
4KAgent: Agentic Any Image to 4K Super-Resolution—0
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
Compressed Video Super-Resolution based on Hierarchical Encoding—0
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
Joint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified