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 1071–1080 of 3874 papers

TitleStatusHype
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
ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems—0
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales—0
Sampling Theory for Super-Resolution with Implicit Neural RepresentationsCode0
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration ModelsCode0
Grids Often Outperform Implicit Neural RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified