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 2581–2590 of 3874 papers

TitleStatusHype
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography—0
High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
Solving Video Inverse Problems Using Image Diffusion Models—0
Some medical applications of example-based super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified