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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2831–2840 of 3874 papers

TitleStatusHype
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Back-Projection Pipeline—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Backdoor Attacks against Image-to-Image Networks—0
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
Redefining Neural Operators in d+1 Dimensions—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Show:102550
← PrevPage 284 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified