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 841–850 of 3874 papers

TitleStatusHype
Edge and Identity Preserving Network for Face Super-ResolutionCode1
PAMS: Quantized Super-Resolution via Parameterized Max ScaleCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Burst Image Restoration and EnhancementCode1
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRICode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified