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 2731–2740 of 3874 papers

TitleStatusHype
Bit-depth color recovery via off-the-shelf super-resolution models—0
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors—0
2D Neural Fields with Learned Discontinuities—0
Pyramidal Denoising Diffusion Probabilistic Models—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Pyramidal Edge-maps and Attention based Guided Thermal Super-resolution—0
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds—0
Pyramid Dual Domain Injection Network for Pan-sharpening—0
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
Binary Diffusion Probabilistic Model—0
Show:102550
← PrevPage 274 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified