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 1021–1030 of 3874 papers

TitleStatusHype
Boosting Video Super Resolution with Patch-Based Temporal Redundancy OptimizationCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified