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 181–190 of 3874 papers

TitleStatusHype
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
NTIRE 2025 Challenge on Image Super-Resolution (4): Methods and ResultsCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact RemovalCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified