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 441–450 of 3874 papers

TitleStatusHype
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified