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 3081–3090 of 3874 papers

TitleStatusHype
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization—0
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation—0
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified