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
Learning Truncated Causal History Model for Video RestorationCode2
PnP-Flow: Plug-and-Play Image Restoration with Flow MatchingCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
HSIGene: A Foundation Model For Hyperspectral Image GenerationCode2
WaveMixSR-V2: Enhancing Super-resolution with Higher EfficiencyCode2
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion ModelsCode2
SSL: A Self-similarity Loss for Improving Generative Image Super-resolutionCode2
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement NetworkCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified