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 3411–3420 of 3874 papers

TitleStatusHype
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural networkCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
A Self-Supervised Deep Denoiser for Hyperspectral and Multispectral Image FusionCode0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image InformationCode0
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR ImagesCode0
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-ResolutionCode0
Wide Activation for Efficient and Accurate Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified