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 1441–1450 of 3874 papers

TitleStatusHype
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion ModelsCode2
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B ImageryCode1
Gated Multi-Resolution Transfer Network for Burst Restoration and EnhancementCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution—0
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Local-Global Temporal Difference Learning for Satellite Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified