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 3311–3320 of 3874 papers

TitleStatusHype
Hierarchical Back Projection Network for Image Super-ResolutionCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
On training deep networks for satellite image super-resolution—0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Show:102550
← PrevPage 332 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified