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 501–510 of 3874 papers

TitleStatusHype
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI SegmentationCode0
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution—0
HASN: Hybrid Attention Separable Network for Efficient Image Super-resolutionCode0
Super Resolution Based on Deep Operator Networks—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Co-learning Single-Step Diffusion Upsampler and Downsampler with Two Discriminators and Distillation—0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
HFH-Font: Few-shot Chinese Font Synthesis with Higher Quality, Faster Speed, and Higher ResolutionCode1
SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-ResolutionCode1
Near-Field ISAC in 6G: Addressing Phase Nonlinearity via Lifted Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified