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 1051–1060 of 3874 papers

TitleStatusHype
Sampling Generative NetworksCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionCode1
Image Super-Resolution Using Deep Convolutional NetworksCode1
Generative Adversarial NetworksCode1
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution—0
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolutionCode0
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
4KAgent: Agentic Any Image to 4K Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified