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 1281–1290 of 3874 papers

TitleStatusHype
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution—0
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual ShiftingCode3
On the Effectiveness of Spectral Discriminators for Perceptual Quality ImprovementCode1
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
Real-Time Neural Video Recovery and Enhancement on Mobile Devices—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network—0
Towards Robust Scene Text Image Super-resolution via Explicit Location EnhancementCode1
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified