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 881–890 of 3874 papers

TitleStatusHype
EBSR: Feature Enhanced Burst Super-Resolution With Deformable AlignmentCode1
Real-World Single Image Super-Resolution: A Brief ReviewCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-ResolutionCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified