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 81–90 of 3874 papers

TitleStatusHype
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space modelsCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-ResolutionCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact RemovalCode2
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified