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 2801–2810 of 3874 papers

TitleStatusHype
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical ZoomCode0
MR Slice Profile Estimation by Learning to Match Internal Patch DistributionsCode0
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural NetworkCode0
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified