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 841–850 of 3874 papers

TitleStatusHype
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for PansharpeningCode1
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified