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 1001–1010 of 3874 papers

TitleStatusHype
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-ResolutionCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural RepresentationCode1
Minimalist and High-Quality Panoramic Imaging with PSF-aware TransformersCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolutionCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified