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 251–260 of 3874 papers

TitleStatusHype
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks—0
Continual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems—0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music GenerationCode5
MFSR-GAN: Multi-Frame Super-Resolution with Handheld Motion Modeling—0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
Delta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified