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 231–240 of 3874 papers

TitleStatusHype
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Burst Image Restoration and EnhancementCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified