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 221–230 of 3874 papers

TitleStatusHype
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-ResolutionCode1
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-ResolutionCode1
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstructionCode1
MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-ResolutionCode1
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-ResolutionCode1
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
Heterogeneous Mixture of Experts for Remote Sensing Image Super-ResolutionCode1
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-ResolutionCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified