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 71–80 of 3874 papers

TitleStatusHype
Visual Autoregressive Modeling for Image Super-ResolutionCode2
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow MatchingCode2
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-ResolutionCode2
AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space modelsCode2
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified