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 61–70 of 3874 papers

TitleStatusHype
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-ResolutionCode2
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
AnimeSR: Learning Real-World Super-Resolution Models for Animation VideosCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-ResolutionCode2
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified